Load libraries (packages)

library("respR") ## respirometry/slope analysis
library("tidyverse") ## data manipulation

Set working directory

setwd("[PATH TO DIRECTORY]")

System1 - Dell

Importing data from firesting for resting

preexperiment_date <- "20 May 2023 11 12AM/All"
postexperiment_date <- "20 May 2023 04 30PM/All"

##--- last fish run in trial ---##
experiment_date <- "20 May 2023 01 11PM/Oxygen"
experiment_date2 <- "20 May 2023 01 11PM/All"

firesting <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_date,"data raw/Firesting.txt"), 
    delim = "\t", escape_double = FALSE, 
    col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"), 
        `Time (s)` = col_number(), Ch1...5 = col_number(), 
        Ch2...6 = col_number(), Ch3...7 = col_number(), 
        Ch4...8 = col_number()), trim_ws = TRUE, 
    skip = 19)

Cycle_1 <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_date2,"slopes/Cycle_1.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 

Cycle_last <-read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_date2,"slopes/Cycle_21.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 

System2 - Asus

Importing data from firesting for resting

preexperiment_date_asus <- "20 May 2023 11 17AM/All"
postexperiment_date_asus <- "20 May 2023 04 19PM/All"

##--- last fish run in trial ---##
experiment_date_asus <- "20 May 2023 12 39PM/Oxygen"
experiment_date2_asus <- "20 May 2023 12 39PM/All"

firesting_asus <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_date_asus,"data raw/Firesting.txt"), 
    delim = "\t", escape_double = FALSE, 
    col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"), 
        `Time (s)` = col_number(), Ch1...5 = col_number(), 
        Ch2...6 = col_number(), Ch3...7 = col_number(), 
        Ch4...8 = col_number()), trim_ws = TRUE, 
    skip = 19)

Cycle_1_asus <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_date2_asus,"slopes/Cycle_1.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 

Cycle_last_asus <-read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_date2_asus,"slopes/Cycle_23.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 

Chamber volumes

chamber1_dell = 0.04650
chamber2_dell = 0.04593
chamber3_dell = 0.04977
chamber4_dell = 0.04860 

chamber1_asus = 0.04565
chamber2_asus = 0.04573
chamber3_asus = 0.04551
chamber4_asus = 0.04791

Date_tested="2023-05-20"
Clutch = "108" 
Male = "CARL369" 
Female = "CARL349"
Population = "Arlington reef"
Tank =216 
salinity =36 
Date_analysed = Sys.Date() 

Replicates

1

Enter specimen data

Replicate = 1 
mass = 0.0006538 
chamber = "ch4" 
Swim = "good/good"
chamber_vol = chamber4_dell
system1 = "Dell"
Notes="check data quailty"

##--- time of trail ---## 
experiment_mmr_date <- "20 May 2023 12 32PM/Oxygen"
experiment_mmr_date2 <- "20 May 2023 12 32PM/All"

firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_mmr_date,"data raw/Firesting.txt"), 
    delim = "\t", escape_double = FALSE, 
    col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"), 
        `Time (s)` = col_number(), Ch1...5 = col_number(), 
        Ch2...6 = col_number(), Ch3...7 = col_number(), 
        Ch4...8 = col_number()), trim_ws = TRUE, 
    skip = 19) 
## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`
## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
##   dat <- vroom(...)
##   problems(dat)
Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_mmr_date2,"slopes/Cycle_1.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 
## New names:
## • `` -> `...8`

Background rates

Pre-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",preexperiment_date,"slopes")) 

pre_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

pre_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

pre_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))


bg_pre1 <- pre_cycle1 %>% calc_rate.bg()
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre2 <- pre_cycle2 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre3 <- pre_cycle3 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre <- mean(bg_pre1$rate.bg.mean,bg_pre2$rate.bg.mean,bg_pre3$rate.bg.mean) 
bg_pre
## [1] -0.002147674

post-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",postexperiment_date,"slopes")) 
 

post_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

post_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

post_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

bg_post1 <- post_cycle1 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post2 <- post_cycle2 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post3 <- post_cycle3 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post <- mean(bg_post1$rate.bg.mean,bg_post2$rate.bg.mean,bg_post3$rate.bg.mean)
bg_post 
## [1] -0.002702356

Resting metabolic rate

Data manipulation

firesting2 <- firesting |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2, time=1, oxygen=2)
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch4
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  7  8  9 10 11 12 13 20 23 28 29 31 32 36 37 38 42 43
## Minimum and Maximum intervals in uneven Time data: 
## [1] 0.95 2.24
## -----------------------------------------

#### subset data

Tstart.row=which(firesting2$TIME ==Cycle_1$Time[1], firesting$TIME) 
Tstart.dTIME=as.numeric(firesting2[Tstart.row, "dTIME"]) 

Tend.row=which(firesting2$TIME ==tail(Cycle_last$Time, n=1), firesting$TIME) 
Tend.dTIME=as.numeric(firesting2[Tend.row, "dTIME"])  

apoly_insp <- firesting2 |> 
  subset_data(from=Tstart.dTIME, 
              to=Tend.dTIME, 
              by="time") 

apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)
## 
## # print.inspect # -----------------------
##                 dTIME  ch4
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]   3   4   8   9  13  14  18  19  20  21 150 152 153 157 158 160 161 164 165
## [20] 169
## Minimum and Maximum intervals in uneven Time data: 
## [1] 0.95 1.77
## -----------------------------------------

Extract rates

apoly_cr.int <- calc_rate.int(apoly_insp, 
                              starts=(195+45+300), 
                              wait=45, 
                              measure=255, 
                              by="time", 
                              plot=TRUE) 
## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...
## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.

## -----------------------------------------
## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from selected replicates... 
## To plot others modify 'pos' input.

## -----------------------------------------

adjust rates for background

apoly_cr.int_adj <- adjust_rate(apoly_cr.int, 
                                by = bg_pre, 
                                by2 = bg_post, 
                                time_by = Tstart.row, 
                                time_by2 = Tend.row,
                                method = "linear")
## adjust_rate: Rate adjustments applied using "linear" method.
apoly_cr.int_adj$summary

Converting units

apoly_cr.int_adj2 <- apoly_cr.int_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253) 
## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.
apoly_cr.int_adj2$summary

Plot curve

ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) + 
  geom_point() + 
  stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
  theme_classic()

Rate filtering

apoly_rmr <- apoly_cr.int_adj2 |> 
  select_rate(method ="rsq", n=c(0.95,1)) |> 
  select_rate(method="lowest", n=6) |> 
  plot(type="full") |> 
  summary(export = TRUE)
## select_rate: Selecting rates with rsq values between 0.95 and 1...
## ----- Selection complete. 0 rate(s) removed, 21 rate(s) remaining -----
## select_rate: Selecting lowest 6 *absolute* rate values...
## ----- Selection complete. 15 rate(s) removed, 6 rate(s) remaining -----
## 
## # plot.convert_rate # -------------------
## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1   rsq density   row endrow     time
## 1:   2    1     240.0145 -0.01823986 0.995      NA   586    841  7770.40
## 2:   5    1     289.8367 -0.02034582 0.992      NA  2197   2452  9390.89
## 3:   8    1     326.9106 -0.02072499 0.998      NA  3817   4072 11010.89
## 4:  11    1     357.0923 -0.02040953 0.994      NA  5437   5692 12630.89
## 5:  12    1     347.5044 -0.01882484 0.988      NA  5977   6232 13170.89
## 6:  20    1     395.4985 -0.01698217 0.996      NA 10297  10552 17490.89
##     endtime    oxy endoxy        rate   adjustment rate.adjusted  rate.input
## 1:  8025.40 98.335 93.386 -0.01823986 -0.002183877   -0.01605598 -0.01605598
## 2:  9645.89 98.521 93.333 -0.02034582 -0.002264921   -0.01808090 -0.01808090
## 3: 11265.89 98.574 93.479 -0.02072499 -0.002345940   -0.01837905 -0.01837905
## 4: 12885.89 98.923 93.990 -0.02040953 -0.002426960   -0.01798257 -0.01798257
## 5: 13425.89 99.241 94.603 -0.01882484 -0.002453966   -0.01637088 -0.01637088
## 6: 17745.89 98.270 93.959 -0.01698217 -0.002670018   -0.01431215 -0.01431215
##    oxy.unit time.unit volume      mass area  S    t        P   rate.abs
## 1:     %Air       sec 0.0486 0.0006538   NA 36 28.5 1.013253 -0.1779560
## 2:     %Air       sec 0.0486 0.0006538   NA 36 28.5 1.013253 -0.2003991
## 3:     %Air       sec 0.0486 0.0006538   NA 36 28.5 1.013253 -0.2037037
## 4:     %Air       sec 0.0486 0.0006538   NA 36 28.5 1.013253 -0.1993094
## 5:     %Air       sec 0.0486 0.0006538   NA 36 28.5 1.013253 -0.1814462
## 6:     %Air       sec 0.0486 0.0006538   NA 36 28.5 1.013253 -0.1586283
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -272.1872          NA  mgO2/hr/kg   -272.1872
## 2:   -306.5144          NA  mgO2/hr/kg   -306.5144
## 3:   -311.5689          NA  mgO2/hr/kg   -311.5689
## 4:   -304.8476          NA  mgO2/hr/kg   -304.8476
## 5:   -277.5255          NA  mgO2/hr/kg   -277.5255
## 6:   -242.6252          NA  mgO2/hr/kg   -242.6252
## -----------------------------------------
## remove lowest slope 
apoly_rmr <- apoly_rmr |> 
  filter(rate.output != max(rate.output))

Results

results <- data.frame(Clutch = Clutch, 
                      Replicate =Replicate, 
                      Male=Male, 
                      Female=Female,
                      Population = Population, 
                      Tank = Tank,
                      Mass = mass, 
                      Chamber = chamber, 
                      System = system1,
                      Volume = chamber_vol, 
                      Date_tested = Date_tested, 
                      Date_analysed =Date_analysed,
                      Swim = Swim,
                      Salinity = salinity, 
                      Temperature = as.numeric(unique(firesting2$temperature)), 
                      Resting_kg = mean(apoly_rmr$rate.output*-1), 
                      Resting =  mean(apoly_rmr$rate.output*-1)*mass, 
                      rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") 
Clutch Replicate Male Female Population Tank Mass Chamber System Volume Date_tested Date_analysed Swim Salinity Temperature Resting_kg Resting rsqrest
108 1 CARL369 CARL349 Arlington reef 216 0.0006538 ch4 Dell 0.0486 2023-05-20 2024-06-14 good/good 36 28.5 294.5287 0.1925629 0.9934

Maximum oxygen consumption

Data manipulation

firesting2_mmr <- firesting_mmr |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_mmr, time=1, oxygen=2)
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch4
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  7  8  9 10 11 12 13 20 23 28 29 31 32 36 37 38 42 43
## Minimum and Maximum intervals in uneven Time data: 
## [1] 0.95 1.90
## -----------------------------------------

Subset data

cycle1.start <-  Cycle_1.mmr[1,1]
cycle1.end <-  tail(Cycle_1.mmr, n=1)[1,1] 

cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start
## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="
cycle1.end.row <- which(firesting2_mmr$TIME == cycle1.end); cycle1.end 
## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="
cycle1_data <- firesting2_mmr |> 
  subset_data(from = cycle1.start.row, 
              to = cycle1.end.row, 
              by = "row") 
## subset_data: Multi-column dataset detected in input! 
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively. 
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.
inspect(cycle1_data)
## inspect: Applying column default of 'time = 1'
## inspect: Applying column default of 'oxygen = 2'
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch4
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1] 10 11 14 15 17 18 19 20 22 23 24 25 26 28 29 30 34 35 36 37
## Minimum and Maximum intervals in uneven Time data: 
## [1] 0.96 1.13
## -----------------------------------------

Calculating MMR

mmr <- auto_rate(cycle1_data, method = "highest", plot=TRUE, width=60, by="time") |> 
  summary()
## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
##   If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()

## 
## # summary.auto_rate # -------------------
## 
## === Summary of Results by Highest Rate ===
##      rep rank intercept_b0    slope_b1       rsq density row endrow    time
##   1:  NA    1     342.2008 -0.04882650 0.9982824      NA 240    300 5111.14
##   2:  NA    2     341.3689 -0.04866484 0.9981693      NA 239    299 5110.01
##   3:  NA    3     340.1123 -0.04842063 0.9980939      NA 238    298 5109.00
##   4:  NA    4     339.7471 -0.04834944 0.9980360      NA 237    297 5108.01
##   5:  NA    5     338.0178 -0.04801305 0.9980117      NA 236    296 5107.00
##  ---                                                                       
## 236:  NA  236     167.9296 -0.01408774 0.9403440      NA   5     65 4876.01
## 237:  NA  237     165.6980 -0.01363287 0.9369329      NA   4     64 4875.01
## 238:  NA  238     162.9844 -0.01307956 0.9396222      NA   3     63 4874.00
## 239:  NA  239     161.0867 -0.01269236 0.9398683      NA   2     62 4873.00
## 240:  NA  240     158.9433 -0.01225523 0.9344615      NA   1     61 4872.00
##      endtime    oxy endoxy        rate
##   1: 5171.14 92.575 89.688 -0.04882650
##   2: 5170.01 92.629 89.746 -0.04866484
##   3: 5169.00 92.673 89.784 -0.04842063
##   4: 5168.01 92.697 89.810 -0.04834944
##   5: 5167.00 92.762 89.856 -0.04801305
##  ---                                  
## 236: 4936.01 99.097 98.260 -0.01408774
## 237: 4935.01 99.160 98.253 -0.01363287
## 238: 4934.00 99.150 98.309 -0.01307956
## 239: 4933.00 99.149 98.354 -0.01269236
## 240: 4932.00 99.099 98.374 -0.01225523
## 
## Regressions : 240 | Results : 240 | Method : highest | Roll width : 60 | Roll type : time 
## -----------------------------------------

Adjusting

mmr_adj <- adjust_rate(mmr, by=bg_pre, method = "mean");mmr_adj
## adjust_rate: Rate adjustments applied using "mean" method.
## 
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
## 
## Adjustment was applied using the 'mean' method.
## 
## Rank 1 of 240 adjusted rate(s):
## Rate          : -0.0488265
## Adjustment    : -0.002147674
## Adjusted Rate : -0.04667882 
## 
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------

Converting units

mmr_adj2 <- mmr_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253)
## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

selecting rates

mmr_final <- mmr_adj2 |> 
  select_rate(method = "rsq", n=c(0.93,1)) |> 
  select_rate(method = "highest", n=1) |> 
  plot(type="full") |> 
  summary(export=TRUE)
## select_rate: Selecting rates with rsq values between 0.93 and 1...
## ----- Selection complete. 0 rate(s) removed, 240 rate(s) remaining -----
## select_rate: Selecting highest 1 *absolute* rate values...
## ----- Selection complete. 239 rate(s) removed, 1 rate(s) remaining -----
## 
## # plot.convert_rate # -------------------
## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0   slope_b1       rsq density row endrow    time
## 1:  NA    1     342.2008 -0.0488265 0.9982824      NA 240    300 5111.14
##    endtime    oxy endoxy       rate   adjustment rate.adjusted  rate.input
## 1: 5171.14 92.575 89.688 -0.0488265 -0.002147674   -0.04667882 -0.04667882
##    oxy.unit time.unit volume      mass area  S    t        P   rate.abs
## 1:     %Air       sec 0.0486 0.0006538   NA 36 28.5 1.013253 -0.5173635
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -791.3176          NA  mgO2/hr/kg   -791.3176
## -----------------------------------------

Results

results <-  results |> 
  mutate(Max_kg = mmr_final$rate.output*-1, 
         Max = (mmr_final$rate.output*-1)*mass, 
         rsqmax =mmr_final$rsq,
         AAS_kg = Max_kg - Resting_kg, 
         AAS = Max - Resting, 
         Notes=Notes, 
                      True_resting="") 
knitr::kable(results, "simple") 
Clutch Replicate Male Female Population Tank Mass Chamber System Volume Date_tested Date_analysed Swim Salinity Temperature Resting_kg Resting rsqrest Max_kg Max rsqmax AAS_kg AAS Notes True_resting
108 1 CARL369 CARL349 Arlington reef 216 0.0006538 ch4 Dell 0.0486 2023-05-20 2024-06-14 good/good 36 28.5 294.5287 0.1925629 0.9934 791.3176 0.5173635 0.9982824 496.7889 0.3248006 check data quailty

Exporting data

resp_results_juveniles <- read_csv("resp_results_juveniles.csv") 
## Rows: 70 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
resp_results_juveniles <- rbind(resp_results_juveniles, results) 
resp_results_juveniles 
write.csv(resp_results_juveniles, file="./resp_results_juveniles.csv", row.names = FALSE)

2

Enter specimen data

Replicate = 2 
mass = 0.0004919 
chamber = "ch3" 
Swim = "good/good"
chamber_vol = chamber3_dell
system1 = "Dell"
Notes="data quailty check"

##--- time of trail ---## 
experiment_mmr_date <- "20 May 2023 12 43PM/Oxygen"
experiment_mmr_date2 <- "20 May 2023 12 43PM/All"

firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_mmr_date,"data raw/Firesting.txt"), 
    delim = "\t", escape_double = FALSE, 
    col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"), 
        `Time (s)` = col_number(), Ch1...5 = col_number(), 
        Ch2...6 = col_number(), Ch3...7 = col_number(), 
        Ch4...8 = col_number()), trim_ws = TRUE, 
    skip = 19) 
## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`
## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
##   dat <- vroom(...)
##   problems(dat)
Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_mmr_date2,"slopes/Cycle_1.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 
## New names:
## • `` -> `...8`

Background rates

Pre-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",preexperiment_date,"slopes")) 

pre_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

pre_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

pre_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))


bg_pre1 <- pre_cycle1 %>% calc_rate.bg()
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre2 <- pre_cycle2 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre3 <- pre_cycle3 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre <- mean(bg_pre1$rate.bg.mean,bg_pre2$rate.bg.mean,bg_pre3$rate.bg.mean) 
bg_pre
## [1] -0.002250613

post-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",postexperiment_date,"slopes")) 
 

post_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

post_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

post_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

bg_post1 <- post_cycle1 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post2 <- post_cycle2 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post3 <- post_cycle3 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post <- mean(bg_post1$rate.bg.mean,bg_post2$rate.bg.mean,bg_post3$rate.bg.mean)
bg_post 
## [1] -0.0031297

Resting metabolic rate

Data manipulation

firesting2 <- firesting |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2, time=1, oxygen=2)
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch3
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  7  8  9 10 11 12 13 20 23 28 29 31 32 36 37 38 42 43
## Minimum and Maximum intervals in uneven Time data: 
## [1] 0.95 2.24
## -----------------------------------------

#### subset data

Tstart.row=which(firesting2$TIME ==Cycle_1$Time[1], firesting$TIME) 
Tstart.dTIME=as.numeric(firesting2[Tstart.row, "dTIME"]) 

Tend.row=which(firesting2$TIME ==tail(Cycle_last$Time, n=1), firesting$TIME) 
Tend.dTIME=as.numeric(firesting2[Tend.row, "dTIME"])  

apoly_insp <- firesting2 |> 
  subset_data(from=Tstart.dTIME, 
              to=Tend.dTIME, 
              by="time") 

apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)
## 
## # print.inspect # -----------------------
##                 dTIME  ch3
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]   3   4   8   9  13  14  18  19  20  21 150 152 153 157 158 160 161 164 165
## [20] 169
## Minimum and Maximum intervals in uneven Time data: 
## [1] 0.95 1.77
## -----------------------------------------

Extract rates

apoly_cr.int <- calc_rate.int(apoly_insp, 
                              starts=(195+45+300), 
                              wait=45, 
                              measure=255, 
                              by="time", 
                              plot=TRUE) 
## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...
## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.

## -----------------------------------------
## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from selected replicates... 
## To plot others modify 'pos' input.

## -----------------------------------------

adjust rates for background

apoly_cr.int_adj <- adjust_rate(apoly_cr.int, 
                                by = bg_pre, 
                                by2 = bg_post, 
                                time_by = Tstart.row, 
                                time_by2 = Tend.row,
                                method = "linear")
## adjust_rate: Rate adjustments applied using "linear" method.
apoly_cr.int_adj$summary

Converting units

apoly_cr.int_adj2 <- apoly_cr.int_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253) 
## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.
apoly_cr.int_adj2$summary

Plot curve

ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) + 
  geom_point() + 
  stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
  theme_classic()

Rate filtering

apoly_rmr <- apoly_cr.int_adj2 |> 
  select_rate(method ="rsq", n=c(0.95,1)) |> 
  select_rate(method="lowest", n=6) |> 
  plot(type="full") |> 
  summary(export = TRUE)
## select_rate: Selecting rates with rsq values between 0.95 and 1...
## ----- Selection complete. 0 rate(s) removed, 21 rate(s) remaining -----
## select_rate: Selecting lowest 6 *absolute* rate values...
## ----- Selection complete. 15 rate(s) removed, 6 rate(s) remaining -----
## 
## # plot.convert_rate # -------------------
## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1   rsq density  row endrow     time
## 1:   1    1     191.1993 -0.01287228 0.996      NA   46    300  7230.40
## 2:   3    1     207.3704 -0.01304214 0.986      NA 1118   1371  8310.37
## 3:   4    1     218.1852 -0.01345980 0.993      NA 1657   1912  8850.89
## 4:  15    1     308.9809 -0.01416776 0.992      NA 7597   7852 14790.89
## 5:  16    1     318.2227 -0.01430074 0.996      NA 8137   8392 15330.89
## 6:  18    1     316.6234 -0.01325879 0.982      NA 9217   9472 16410.89
##     endtime    oxy endoxy        rate   adjustment rate.adjusted  rate.input
## 1:  7485.11 98.157 94.713 -0.01287228 -0.002265177   -0.01060710 -0.01060710
## 2:  8564.89 98.840 95.373 -0.01304214 -0.002350770   -0.01069137 -0.01069137
## 3:  9105.89 98.959 95.335 -0.01345980 -0.002393631   -0.01106617 -0.01106617
## 4: 15045.89 99.275 95.731 -0.01416776 -0.002864443   -0.01130332 -0.01130332
## 5: 15585.89 98.894 95.321 -0.01430074 -0.002907244   -0.01139349 -0.01139349
## 6: 16665.89 98.822 95.260 -0.01325879 -0.002992846   -0.01026594 -0.01026594
##    oxy.unit time.unit  volume      mass area  S    t        P   rate.abs
## 1:     %Air       sec 0.04977 0.0004919   NA 36 28.5 1.013253 -0.1203937
## 2:     %Air       sec 0.04977 0.0004919   NA 36 28.5 1.013253 -0.1213502
## 3:     %Air       sec 0.04977 0.0004919   NA 36 28.5 1.013253 -0.1256043
## 4:     %Air       sec 0.04977 0.0004919   NA 36 28.5 1.013253 -0.1282960
## 5:     %Air       sec 0.04977 0.0004919   NA 36 28.5 1.013253 -0.1293195
## 6:     %Air       sec 0.04977 0.0004919   NA 36 28.5 1.013253 -0.1165215
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -244.7525          NA  mgO2/hr/kg   -244.7525
## 2:   -246.6969          NA  mgO2/hr/kg   -246.6969
## 3:   -255.3451          NA  mgO2/hr/kg   -255.3451
## 4:   -260.8173          NA  mgO2/hr/kg   -260.8173
## 5:   -262.8980          NA  mgO2/hr/kg   -262.8980
## 6:   -236.8805          NA  mgO2/hr/kg   -236.8805
## -----------------------------------------
## remove lowest slope 
apoly_rmr <- apoly_rmr |> 
  filter(rate.output != max(rate.output))

Results

results <- data.frame(Clutch = Clutch, 
                      Replicate =Replicate, 
                      Male=Male, 
                      Female=Female,
                      Population = Population, 
                      Tank = Tank,
                      Mass = mass, 
                      Chamber = chamber, 
                      System = system1,
                      Volume = chamber_vol, 
                      Date_tested = Date_tested, 
                      Date_analysed =Date_analysed,
                      Swim = Swim,
                      Salinity = salinity, 
                      Temperature = as.numeric(unique(firesting2$temperature)), 
                      Resting_kg = mean(apoly_rmr$rate.output*-1), 
                      Resting =  mean(apoly_rmr$rate.output*-1)*mass, 
                      rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") 
Clutch Replicate Male Female Population Tank Mass Chamber System Volume Date_tested Date_analysed Swim Salinity Temperature Resting_kg Resting rsqrest
108 2 CARL369 CARL349 Arlington reef 216 0.0004919 ch3 Dell 0.04977 2023-05-20 2024-06-14 good/good 36 28.5 254.1019 0.1249927 0.9926

Maximum oxygen consumption

Data manipulation

firesting2_mmr <- firesting_mmr |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_mmr, time=1, oxygen=2)
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch3
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  7  8  9 10 11 12 13 20 23 28 29 31 32 36 37 38 42 43
## Minimum and Maximum intervals in uneven Time data: 
## [1] 0.95 2.24
## -----------------------------------------

Subset data

cycle1.start <-  Cycle_1.mmr[1,1]
cycle1.end <-  tail(Cycle_1.mmr, n=1)[1,1] 

cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start
## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="
cycle1.end.row <- which(firesting2_mmr$TIME == cycle1.end); cycle1.end 
## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="
cycle1_data <- firesting2_mmr |> 
  subset_data(from = cycle1.start.row, 
              to = cycle1.end.row,  
              by = "row") 
## subset_data: Multi-column dataset detected in input! 
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively. 
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.
inspect(cycle1_data)
## inspect: Applying column default of 'time = 1'
## inspect: Applying column default of 'oxygen = 2'
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch3
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  2  3  7  8  9 13 14 15 17 18 19 20 22 23 24 25 26 27 28 29
## Minimum and Maximum intervals in uneven Time data: 
## [1] 0.95 1.31
## -----------------------------------------

Calculating MMR

mmr <- auto_rate(cycle1_data, method = "highest", plot=TRUE, width=60, by="time") |> 
  summary()
## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
##   If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()

## 
## # summary.auto_rate # -------------------
## 
## === Summary of Results by Highest Rate ===
##      rep rank intercept_b0     slope_b1       rsq density row endrow    time
##   1:  NA    1     246.6569 -0.026200119 0.9758343      NA 239    299 5726.52
##   2:  NA    2     244.5290 -0.025830520 0.9746073      NA 238    298 5725.57
##   3:  NA    3     242.4313 -0.025466123 0.9733300      NA 237    297 5724.50
##   4:  NA    4     240.1233 -0.025064899 0.9732530      NA 236    296 5723.53
##   5:  NA    5     238.0609 -0.024706317 0.9729476      NA 235    295 5722.56
##  ---                                                                        
## 235:  NA  235     128.7821 -0.005393654 0.7888607      NA  31     91 5516.93
## 236:  NA  236     128.4337 -0.005330672 0.7872523      NA  30     90 5515.93
## 237:  NA  237     128.1541 -0.005280112 0.7834936      NA  27     87 5512.93
## 238:  NA  238     127.7295 -0.005203619 0.7814666      NA  29     89 5514.93
## 239:  NA  239     127.7191 -0.005201793 0.7812818      NA  28     88 5513.93
##      endtime    oxy endoxy         rate
##   1: 5786.52 96.519 94.957 -0.026200119
##   2: 5785.57 96.557 94.994 -0.025830520
##   3: 5784.50 96.578 94.987 -0.025466123
##   4: 5783.53 96.552 95.043 -0.025064899
##   5: 5782.56 96.523 95.100 -0.024706317
##  ---                                   
## 235: 5576.93 99.029 98.658 -0.005393654
## 236: 5575.93 99.037 98.660 -0.005330672
## 237: 5572.93 99.093 98.723 -0.005280112
## 238: 5574.93 99.007 98.729 -0.005203619
## 239: 5573.93 99.026 98.723 -0.005201793
## 
## Regressions : 239 | Results : 239 | Method : highest | Roll width : 60 | Roll type : time 
## -----------------------------------------

Adjusting

mmr_adj <- adjust_rate(mmr, by=bg_pre, method = "mean");mmr_adj
## adjust_rate: Rate adjustments applied using "mean" method.
## 
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
## 
## Adjustment was applied using the 'mean' method.
## 
## Rank 1 of 239 adjusted rate(s):
## Rate          : -0.02620012
## Adjustment    : -0.002250613
## Adjusted Rate : -0.02394951 
## 
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------

Converting units

mmr_adj2 <- mmr_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253)
## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

selecting rates

mmr_final <- mmr_adj2 |> 
  select_rate(method = "rsq", n=c(0.93,1)) |> 
  select_rate(method = "highest", n=1) |> 
  plot(type="full") |> 
  summary(export=TRUE)
## select_rate: Selecting rates with rsq values between 0.93 and 1...
## ----- Selection complete. 116 rate(s) removed, 123 rate(s) remaining -----
## select_rate: Selecting highest 1 *absolute* rate values...
## ----- Selection complete. 122 rate(s) removed, 1 rate(s) remaining -----
## 
## # plot.convert_rate # -------------------
## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1       rsq density row endrow    time
## 1:  NA    1     246.6569 -0.02620012 0.9758343      NA 239    299 5726.52
##    endtime    oxy endoxy        rate   adjustment rate.adjusted  rate.input
## 1: 5786.52 96.519 94.957 -0.02620012 -0.002250613   -0.02394951 -0.02394951
##    oxy.unit time.unit  volume      mass area  S    t        P  rate.abs
## 1:     %Air       sec 0.04977 0.0004919   NA 36 28.5 1.013253 -0.271834
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -552.6205          NA  mgO2/hr/kg   -552.6205
## -----------------------------------------

Results

results <-  results |> 
  mutate(Max_kg = mmr_final$rate.output*-1, 
         Max = (mmr_final$rate.output*-1)*mass, 
         rsqmax =mmr_final$rsq,
         AAS_kg = Max_kg - Resting_kg, 
         AAS = Max - Resting, 
                      Notes=Notes, 
                      True_resting="") 
knitr::kable(results, "simple") 
Clutch Replicate Male Female Population Tank Mass Chamber System Volume Date_tested Date_analysed Swim Salinity Temperature Resting_kg Resting rsqrest Max_kg Max rsqmax AAS_kg AAS Notes True_resting
108 2 CARL369 CARL349 Arlington reef 216 0.0004919 ch3 Dell 0.04977 2023-05-20 2024-06-14 good/good 36 28.5 254.1019 0.1249927 0.9926 552.6205 0.271834 0.9758343 298.5185 0.1468413 data quailty check

Exporting data

resp_results_juveniles <- read_csv("resp_results_juveniles.csv") 
## Rows: 71 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
resp_results_juveniles <- rbind(resp_results_juveniles, results) 
resp_results_juveniles 
write.csv(resp_results_juveniles, file="./resp_results_juveniles.csv", row.names = FALSE)

3

Enter specimen data

Replicate = 3 
mass = 0.0003905 
chamber = "ch2" 
Swim = "good/good"
chamber_vol = chamber2_dell
system1 = "Dell"
Notes=""

##--- time of trail ---## 
experiment_mmr_date <- "20 May 2023 01 01PM/Oxygen"
experiment_mmr_date2 <- "20 May 2023 01 01PM/All"

firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_mmr_date,"data raw/Firesting.txt"), 
    delim = "\t", escape_double = FALSE, 
    col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"), 
        `Time (s)` = col_number(), Ch1...5 = col_number(), 
        Ch2...6 = col_number(), Ch3...7 = col_number(), 
        Ch4...8 = col_number()), trim_ws = TRUE, 
    skip = 19) 
## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`
## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
##   dat <- vroom(...)
##   problems(dat)
Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_mmr_date2,"slopes/Cycle_1.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 
## New names:
## • `` -> `...8`

Background rates

Pre-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",preexperiment_date,"slopes")) 

pre_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

pre_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

pre_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))


bg_pre1 <- pre_cycle1 %>% calc_rate.bg()
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre2 <- pre_cycle2 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre3 <- pre_cycle3 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre <- mean(bg_pre1$rate.bg.mean,bg_pre2$rate.bg.mean,bg_pre3$rate.bg.mean) 
bg_pre
## [1] -0.001682967

post-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",postexperiment_date,"slopes")) 
 

post_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

post_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

post_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

bg_post1 <- post_cycle1 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post2 <- post_cycle2 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post3 <- post_cycle3 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post <- mean(bg_post1$rate.bg.mean,bg_post2$rate.bg.mean,bg_post3$rate.bg.mean)
bg_post 
## [1] -0.004041426

Resting metabolic rate

Data manipulation

firesting2 <- firesting |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2, time=1, oxygen=2)
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch2
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  7  8  9 10 11 12 13 20 23 28 29 31 32 36 37 38 42 43
## Minimum and Maximum intervals in uneven Time data: 
## [1] 0.95 2.24
## -----------------------------------------

#### subset data

Tstart.row=which(firesting2$TIME ==Cycle_1$Time[1], firesting$TIME) 
Tstart.dTIME=as.numeric(firesting2[Tstart.row, "dTIME"]) 

Tend.row=which(firesting2$TIME ==tail(Cycle_last$Time, n=1), firesting$TIME) 
Tend.dTIME=as.numeric(firesting2[Tend.row, "dTIME"])  

apoly_insp <- firesting2 |> 
  subset_data(from=Tstart.dTIME, 
              to=Tend.dTIME, 
              by="time") 

apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)
## 
## # print.inspect # -----------------------
##                 dTIME  ch2
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]   3   4   8   9  13  14  18  19  20  21 150 152 153 157 158 160 161 164 165
## [20] 169
## Minimum and Maximum intervals in uneven Time data: 
## [1] 0.95 1.77
## -----------------------------------------

Extract rates

apoly_cr.int <- calc_rate.int(apoly_insp, 
                              starts=(195+45+300), 
                              wait=45, 
                              measure=255, 
                              by="time", 
                              plot=TRUE) 
## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...
## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.

## -----------------------------------------
## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from selected replicates... 
## To plot others modify 'pos' input.

## -----------------------------------------

adjust rates for background

apoly_cr.int_adj <- adjust_rate(apoly_cr.int, 
                                by = bg_pre, 
                                by2 = bg_post, 
                                time_by = Tstart.row, 
                                time_by2 = Tend.row,
                                method = "linear")
## adjust_rate: Rate adjustments applied using "linear" method.
apoly_cr.int_adj$summary

Converting units

apoly_cr.int_adj2 <- apoly_cr.int_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253) 
## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.
apoly_cr.int_adj2$summary

Plot curve

ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) + 
  geom_point() + 
  stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
  theme_classic()

Rate filtering

apoly_rmr <- apoly_cr.int_adj2 |> 
  select_rate(method ="rsq", n=c(0.95,1)) |> 
  select_rate(method="lowest", n=6) |> 
  plot(type="full") |> 
  summary(export = TRUE)
## select_rate: Selecting rates with rsq values between 0.95 and 1...
## ----- Selection complete. 2 rate(s) removed, 19 rate(s) remaining -----
## select_rate: Selecting lowest 6 *absolute* rate values...
## ----- Selection complete. 13 rate(s) removed, 6 rate(s) remaining -----
## 
## # plot.convert_rate # -------------------
## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1   rsq density  row endrow     time
## 1:   1    1     210.5092 -0.01537315 0.978      NA   46    300  7230.40
## 2:   2    1     214.5733 -0.01471256 0.962      NA  586    841  7770.40
## 3:   4    1     236.6494 -0.01541790 0.989      NA 1657   1912  8850.89
## 4:  12    1     290.0547 -0.01433983 0.975      NA 5977   6232 13170.89
## 5:  13    1     339.2149 -0.01740442 0.995      NA 6517   6772 13710.89
## 6:  18    1     363.0989 -0.01601507 0.993      NA 9217   9472 16410.89
##     endtime     oxy endoxy        rate   adjustment rate.adjusted  rate.input
## 1:  7485.11  99.071 95.296 -0.01537315 -0.001722042   -0.01365111 -0.01365111
## 2:  8025.40  99.945 96.238 -0.01471256 -0.001836901   -0.01287566 -0.01287566
## 3:  9105.89 100.120 96.139 -0.01541790 -0.002066664   -0.01335123 -0.01335123
## 4: 13425.89 101.170 97.126 -0.01433983 -0.002985295   -0.01135453 -0.01135453
## 5: 13965.89 100.300 96.028 -0.01740442 -0.003100124   -0.01430430 -0.01430430
## 6: 16665.89 100.240 96.122 -0.01601507 -0.003674269   -0.01234080 -0.01234080
##    oxy.unit time.unit  volume      mass area  S    t        P   rate.abs
## 1:     %Air       sec 0.04593 0.0003905   NA 36 28.5 1.013253 -0.1429894
## 2:     %Air       sec 0.04593 0.0003905   NA 36 28.5 1.013253 -0.1348670
## 3:     %Air       sec 0.04593 0.0003905   NA 36 28.5 1.013253 -0.1398484
## 4:     %Air       sec 0.04593 0.0003905   NA 36 28.5 1.013253 -0.1189338
## 5:     %Air       sec 0.04593 0.0003905   NA 36 28.5 1.013253 -0.1498313
## 6:     %Air       sec 0.04593 0.0003905   NA 36 28.5 1.013253 -0.1292646
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -366.1701          NA  mgO2/hr/kg   -366.1701
## 2:   -345.3699          NA  mgO2/hr/kg   -345.3699
## 3:   -358.1264          NA  mgO2/hr/kg   -358.1264
## 4:   -304.5680          NA  mgO2/hr/kg   -304.5680
## 5:   -383.6910          NA  mgO2/hr/kg   -383.6910
## 6:   -331.0232          NA  mgO2/hr/kg   -331.0232
## -----------------------------------------
## remove lowest slope 
apoly_rmr <- apoly_rmr |> 
  filter(rate.output != max(rate.output))

Results

results <- data.frame(Clutch = Clutch, 
                      Replicate =Replicate, 
                      Male=Male, 
                      Female=Female,
                      Population = Population, 
                      Tank = Tank,
                      Mass = mass, 
                      Chamber = chamber, 
                      System = system1,
                      Volume = chamber_vol, 
                      Date_tested = Date_tested, 
                      Date_analysed =Date_analysed,
                      Swim = Swim,
                      Salinity = salinity, 
                      Temperature = as.numeric(unique(firesting2$temperature)), 
                      Resting_kg = mean(apoly_rmr$rate.output*-1), 
                      Resting =  mean(apoly_rmr$rate.output*-1)*mass, 
                      rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple")  
Clutch Replicate Male Female Population Tank Mass Chamber System Volume Date_tested Date_analysed Swim Salinity Temperature Resting_kg Resting rsqrest
108 3 CARL369 CARL349 Arlington reef 216 0.0003905 ch2 Dell 0.04593 2023-05-20 2024-06-14 good/good 36 28.5 356.8761 0.1393601 0.9834

Maximum oxygen consumption

Data manipulation

firesting2_mmr <- firesting_mmr |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_mmr, time=1, oxygen=2)
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch2
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  7  8  9 10 11 12 13 20 23 28 29 31 32 36 37 38 42 43
## Minimum and Maximum intervals in uneven Time data: 
## [1] 0.95 2.24
## -----------------------------------------

Subset data

cycle1.start <-  Cycle_1.mmr[1,1]
cycle1.end <-  tail(Cycle_1.mmr, n=1)[1,1] 

cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start
## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="
cycle1.end.row <- which(firesting2_mmr$TIME == cycle1.end); cycle1.end 
## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="
cycle1_data <- firesting2_mmr |> 
  subset_data(from = cycle1.start.row, 
              to = cycle1.end.row, 
              by = "row") 
## subset_data: Multi-column dataset detected in input! 
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively. 
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.
inspect(cycle1_data)
## inspect: Applying column default of 'time = 1'
## inspect: Applying column default of 'oxygen = 2'
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch2
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1] 40 41 60 61 65 66 70 71 75 76 80 81 83 84 87 88 92 93 97 98
## Minimum and Maximum intervals in uneven Time data: 
## [1] 0.96 1.06
## -----------------------------------------

Calculating MMR

mmr <- auto_rate(cycle1_data, method = "highest", plot=TRUE, width=60, by="time") |> 
  summary()
## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
##   If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()

## 
## # summary.auto_rate # -------------------
## 
## === Summary of Results by Highest Rate ===
##      rep rank intercept_b0     slope_b1       rsq density row endrow    time
##   1:  NA    1     348.1903 -0.037448630 0.9674151      NA  54    114 6635.40
##   2:  NA    2     347.9538 -0.037412334 0.9670888      NA  55    115 6636.40
##   3:  NA    3     347.7993 -0.037388399 0.9668829      NA  56    116 6637.40
##   4:  NA    4     347.1741 -0.037297042 0.9659668      NA  53    113 6634.40
##   5:  NA    5     346.7394 -0.037232682 0.9653473      NA  52    112 6633.40
##  ---                                                                        
## 237:  NA  237     148.7907 -0.007427090 0.8729088      NA   3     63 6584.40
## 238:  NA  238     148.7238 -0.007417133 0.8669799      NA   1     61 6582.42
## 239:  NA  239     148.6768 -0.007409307 0.8631370      NA   6     66 6587.41
## 240:  NA  240     148.6458 -0.007405058 0.8716729      NA   4     64 6585.40
## 241:  NA  241     148.3977 -0.007367745 0.8704325      NA   2     62 6583.40
##      endtime    oxy endoxy         rate
##   1: 6695.40 99.552 97.588 -0.037448630
##   2: 6696.40 99.525 97.599 -0.037412334
##   3: 6697.40 99.565 97.554 -0.037388399
##   4: 6694.40 99.510 97.650 -0.037297042
##   5: 6693.40 99.565 97.708 -0.037232682
##  ---                                   
## 237: 6644.40 99.866 99.437 -0.007427090
## 238: 6642.42 99.894 99.528 -0.007417133
## 239: 6647.41 99.823 99.506 -0.007409307
## 240: 6645.40 99.864 99.471 -0.007405058
## 241: 6643.40 99.863 99.480 -0.007367745
## 
## Regressions : 241 | Results : 241 | Method : highest | Roll width : 60 | Roll type : time 
## -----------------------------------------

Adjusting

mmr_adj <- adjust_rate(mmr, by=bg_pre, method = "mean");mmr_adj
## adjust_rate: Rate adjustments applied using "mean" method.
## 
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
## 
## Adjustment was applied using the 'mean' method.
## 
## Rank 1 of 241 adjusted rate(s):
## Rate          : -0.03744863
## Adjustment    : -0.001682967
## Adjusted Rate : -0.03576566 
## 
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------

Converting units

mmr_adj2 <- mmr_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253)
## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

selecting rates

mmr_final <- mmr_adj2 |> 
  select_rate(method = "rsq", n=c(0.93,1)) |> 
  select_rate(method = "highest", n=1) |> 
  plot(type="full") |> 
  summary(export=TRUE)
## select_rate: Selecting rates with rsq values between 0.93 and 1...
## ----- Selection complete. 81 rate(s) removed, 160 rate(s) remaining -----
## select_rate: Selecting highest 1 *absolute* rate values...
## ----- Selection complete. 159 rate(s) removed, 1 rate(s) remaining -----
## 
## # plot.convert_rate # -------------------
## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1       rsq density row endrow   time
## 1:  NA    1     348.1903 -0.03744863 0.9674151      NA  54    114 6635.4
##    endtime    oxy endoxy        rate   adjustment rate.adjusted  rate.input
## 1:  6695.4 99.552 97.588 -0.03744863 -0.001682967   -0.03576566 -0.03576566
##    oxy.unit time.unit  volume      mass area  S    t        P   rate.abs
## 1:     %Air       sec 0.04593 0.0003905   NA 36 28.5 1.013253 -0.3746298
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -959.3593          NA  mgO2/hr/kg   -959.3593
## -----------------------------------------

Results

results <-  results |> 
  mutate(Max_kg = mmr_final$rate.output*-1, 
         Max = (mmr_final$rate.output*-1)*mass, 
         rsqmax =mmr_final$rsq,
         AAS_kg = Max_kg - Resting_kg, 
         AAS = Max - Resting, 
                      Notes=Notes, 
                      True_resting="") 
knitr::kable(results, "simple") 
Clutch Replicate Male Female Population Tank Mass Chamber System Volume Date_tested Date_analysed Swim Salinity Temperature Resting_kg Resting rsqrest Max_kg Max rsqmax AAS_kg AAS Notes True_resting
108 3 CARL369 CARL349 Arlington reef 216 0.0003905 ch2 Dell 0.04593 2023-05-20 2024-06-14 good/good 36 28.5 356.8761 0.1393601 0.9834 959.3593 0.3746298 0.9674151 602.4831 0.2352697

Exporting data

resp_results_juveniles <- read_csv("resp_results_juveniles.csv") 
## Rows: 72 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
resp_results_juveniles <- rbind(resp_results_juveniles, results) 
resp_results_juveniles 
write.csv(resp_results_juveniles, file="./resp_results_juveniles.csv", row.names = FALSE)

4

Enter specimen data

Replicate = 4 
mass = 0.0006171 
chamber = "ch1" 
Swim = "good/good"
chamber_vol = chamber1_dell
system1 = "Dell"
Notes=""

##--- time of trail ---## 
experiment_mmr_date <- "20 May 2023 01 11PM/Oxygen"
experiment_mmr_date2 <- "20 May 2023 01 11PM/All"

firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_mmr_date,"data raw/Firesting.txt"), 
    delim = "\t", escape_double = FALSE, 
    col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"), 
        `Time (s)` = col_number(), Ch1...5 = col_number(), 
        Ch2...6 = col_number(), Ch3...7 = col_number(), 
        Ch4...8 = col_number()), trim_ws = TRUE, 
    skip = 19) 
## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`
## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
##   dat <- vroom(...)
##   problems(dat)
Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_mmr_date2,"slopes/Cycle_1.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 
## New names:
## • `` -> `...8`

Background rates

Pre-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",preexperiment_date,"slopes")) 

pre_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

pre_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

pre_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))


bg_pre1 <- pre_cycle1 %>% calc_rate.bg()
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre2 <- pre_cycle2 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre3 <- pre_cycle3 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre <- mean(bg_pre1$rate.bg.mean,bg_pre2$rate.bg.mean,bg_pre3$rate.bg.mean) 
bg_pre
## [1] -0.001461925

post-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",postexperiment_date,"slopes")) 
 

post_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

post_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

post_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

bg_post1 <- post_cycle1 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post2 <- post_cycle2 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post3 <- post_cycle3 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post <- mean(bg_post1$rate.bg.mean,bg_post2$rate.bg.mean,bg_post3$rate.bg.mean)
bg_post 
## [1] -0.002342369

Resting metabolic rate

Data manipulation

firesting2 <- firesting |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2, time=1, oxygen=2)
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch1
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  7  8  9 10 11 12 13 20 23 28 29 31 32 36 37 38 42 43
## Minimum and Maximum intervals in uneven Time data: 
## [1] 0.95 2.24
## -----------------------------------------

#### subset data

Tstart.row=which(firesting2$TIME ==Cycle_1$Time[1], firesting$TIME) 
Tstart.dTIME=as.numeric(firesting2[Tstart.row, "dTIME"]) 

Tend.row=which(firesting2$TIME ==tail(Cycle_last$Time, n=1), firesting$TIME) 
Tend.dTIME=as.numeric(firesting2[Tend.row, "dTIME"])  

apoly_insp <- firesting2 |> 
  subset_data(from=Tstart.dTIME, 
              to=Tend.dTIME, 
              by="time") 

apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)
## 
## # print.inspect # -----------------------
##                 dTIME  ch1
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]   3   4   8   9  13  14  18  19  20  21 150 152 153 157 158 160 161 164 165
## [20] 169
## Minimum and Maximum intervals in uneven Time data: 
## [1] 0.95 1.77
## -----------------------------------------

Extract rates

apoly_cr.int <- calc_rate.int(apoly_insp, 
                              starts=(195+45+300), 
                              wait=45, 
                              measure=255, 
                              by="time", 
                              plot=TRUE)  
## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...
## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.

## -----------------------------------------
## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from selected replicates... 
## To plot others modify 'pos' input.

## -----------------------------------------

adjust rates for background

apoly_cr.int_adj <- adjust_rate(apoly_cr.int, 
                                by = bg_pre, 
                                by2 = bg_post, 
                                time_by = Tstart.row, 
                                time_by2 = Tend.row,
                                method = "linear")
## adjust_rate: Rate adjustments applied using "linear" method.
apoly_cr.int_adj$summary

Converting units

apoly_cr.int_adj2 <- apoly_cr.int_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253) 
## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.
apoly_cr.int_adj2$summary

Plot curve

ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) + 
  geom_point() + 
  stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
  theme_classic()

Rate filtering

apoly_rmr <- apoly_cr.int_adj2 |> 
  select_rate(method ="rsq", n=c(0.95,1)) |> 
  select_rate(method="lowest", n=6) |> 
  plot(type="full") |> 
  summary(export = TRUE)
## select_rate: Selecting rates with rsq values between 0.95 and 1...
## ----- Selection complete. 0 rate(s) removed, 21 rate(s) remaining -----
## select_rate: Selecting lowest 6 *absolute* rate values...
## ----- Selection complete. 15 rate(s) removed, 6 rate(s) remaining -----
## 
## # plot.convert_rate # -------------------
## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1   rsq density   row endrow     time
## 1:   9    1     445.3517 -0.03000890 0.999      NA  4357   4612 11550.89
## 2:  13    1     487.7320 -0.02842405 0.998      NA  6517   6772 13710.89
## 3:  16    1     546.7068 -0.02925229 0.993      NA  8137   8392 15330.89
## 4:  18    1     502.8789 -0.02466506 0.991      NA  9217   9472 16410.89
## 5:  19    1     581.3047 -0.02848321 0.993      NA  9757  10012 16950.89
## 6:  20    1     610.8256 -0.02932990 0.998      NA 10297  10552 17490.89
##     endtime    oxy endoxy        rate   adjustment rate.adjusted  rate.input
## 1: 11805.89 98.672 90.842 -0.03000890 -0.001819500   -0.02818940 -0.02818940
## 2: 13965.89 98.015 90.615 -0.02842405 -0.001990969   -0.02643309 -0.02643309
## 3: 15585.89 98.260 90.507 -0.02925229 -0.002119570   -0.02713272 -0.02713272
## 4: 16665.89 98.103 91.980 -0.02466506 -0.002205304   -0.02245975 -0.02245975
## 5: 17205.89 98.281 90.977 -0.02848321 -0.002248172   -0.02623504 -0.02623504
## 6: 17745.89 97.791 90.261 -0.02932990 -0.002291039   -0.02703886 -0.02703886
##    oxy.unit time.unit volume      mass area  S    t        P   rate.abs
## 1:     %Air       sec 0.0465 0.0006171   NA 36 28.5 1.013253 -0.2989361
## 2:     %Air       sec 0.0465 0.0006171   NA 36 28.5 1.013253 -0.2803112
## 3:     %Air       sec 0.0465 0.0006171   NA 36 28.5 1.013253 -0.2877305
## 4:     %Air       sec 0.0465 0.0006171   NA 36 28.5 1.013253 -0.2381757
## 5:     %Air       sec 0.0465 0.0006171   NA 36 28.5 1.013253 -0.2782110
## 6:     %Air       sec 0.0465 0.0006171   NA 36 28.5 1.013253 -0.2867352
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -484.4209          NA  mgO2/hr/kg   -484.4209
## 2:   -454.2395          NA  mgO2/hr/kg   -454.2395
## 3:   -466.2624          NA  mgO2/hr/kg   -466.2624
## 4:   -385.9597          NA  mgO2/hr/kg   -385.9597
## 5:   -450.8361          NA  mgO2/hr/kg   -450.8361
## 6:   -464.6495          NA  mgO2/hr/kg   -464.6495
## -----------------------------------------
## remove lowest slope 
apoly_rmr <- apoly_rmr |> 
  filter(rate.output != max(rate.output))

Results

results <- data.frame(Clutch = Clutch, 
                      Replicate =Replicate, 
                      Male=Male, 
                      Female=Female,
                      Population = Population, 
                      Tank = Tank,
                      Mass = mass, 
                      Chamber = chamber, 
                      System = system1,
                      Volume = chamber_vol, 
                      Date_tested = Date_tested, 
                      Date_analysed =Date_analysed,
                      Swim = Swim,
                      Salinity = salinity, 
                      Temperature = as.numeric(unique(firesting2$temperature)), 
                      Resting_kg = mean(apoly_rmr$rate.output*-1), 
                      Resting =  mean(apoly_rmr$rate.output*-1)*mass, 
                      rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple")  
Clutch Replicate Male Female Population Tank Mass Chamber System Volume Date_tested Date_analysed Swim Salinity Temperature Resting_kg Resting rsqrest
108 4 CARL369 CARL349 Arlington reef 216 0.0006171 ch1 Dell 0.0465 2023-05-20 2024-06-14 good/good 36 28.5 464.0817 0.2863848 0.9962

Maximum oxygen consumption

Data manipulation

firesting2_mmr <- firesting_mmr |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_mmr, time=1, oxygen=2)
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch1
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  7  8  9 10 11 12 13 20 23 28 29 31 32 36 37 38 42 43
## Minimum and Maximum intervals in uneven Time data: 
## [1] 0.95 2.24
## -----------------------------------------

Subset data

cycle1.start <-  Cycle_1.mmr[1,1]
cycle1.end <-  tail(Cycle_1.mmr, n=1)[1,1] 

cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start
## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="
cycle1.end.row <- which(firesting2_mmr$TIME == cycle1.end); cycle1.end 
## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="
cycle1_data <- firesting2_mmr |> 
  subset_data(from = cycle1.start.row, 
              to = cycle1.end.row, 
              by = "row") 
## subset_data: Multi-column dataset detected in input! 
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively. 
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.
inspect(cycle1_data)
## inspect: Applying column default of 'time = 1'
## inspect: Applying column default of 'oxygen = 2'
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch1
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]   3   4   8   9  13  14  18  19  20  21 150 152 153 157 158 160 161 164 165
## [20] 169
## Minimum and Maximum intervals in uneven Time data: 
## [1] 0.95 1.25
## -----------------------------------------

Calculating MMR

mmr <- auto_rate(cycle1_data, method = "highest", plot=TRUE, width=60, by="time") |> 
  summary()
## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
##   If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()

## 
## # summary.auto_rate # -------------------
## 
## === Summary of Results by Highest Rate ===
##      rep rank intercept_b0    slope_b1       rsq density row endrow    time
##   1:  NA    1     543.1568 -0.06159952 0.9971818      NA 105    165 7289.43
##   2:  NA    2     542.1673 -0.06146493 0.9972104      NA 104    164 7288.40
##   3:  NA    3     542.0061 -0.06144210 0.9971756      NA 106    166 7290.40
##   4:  NA    4     541.7068 -0.06140247 0.9970885      NA 103    163 7287.40
##   5:  NA    5     541.1947 -0.06133077 0.9969525      NA 107    167 7291.40
##  ---                                                                       
## 236:  NA  236     305.4091 -0.02932342 0.9756596      NA 221    281 7406.33
## 237:  NA  237     304.9885 -0.02926427 0.9763404      NA 217    277 7402.09
## 238:  NA  238     304.2585 -0.02916786 0.9773218      NA 220    280 7405.28
## 239:  NA  239     304.1078 -0.02914638 0.9774276      NA 218    278 7403.13
## 240:  NA  240     303.0228 -0.02900101 0.9787239      NA 219    279 7404.22
##      endtime    oxy endoxy        rate
##   1: 7349.43 94.052 90.528 -0.06159952
##   2: 7348.40 94.093 90.586 -0.06146493
##   3: 7350.40 93.993 90.475 -0.06144210
##   4: 7347.40 94.118 90.594 -0.06140247
##   5: 7351.40 93.964 90.469 -0.06133077
##  ---                                  
## 236: 7466.33 88.394 86.251 -0.02932342
## 237: 7462.09 88.598 86.557 -0.02926427
## 238: 7465.28 88.381 86.327 -0.02916786
## 239: 7463.13 88.531 86.486 -0.02914638
## 240: 7464.22 88.497 86.467 -0.02900101
## 
## Regressions : 240 | Results : 240 | Method : highest | Roll width : 60 | Roll type : time 
## -----------------------------------------

Adjusting

mmr_adj <- adjust_rate(mmr, by=bg_pre, method = "mean");mmr_adj
## adjust_rate: Rate adjustments applied using "mean" method.
## 
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
## 
## Adjustment was applied using the 'mean' method.
## 
## Rank 1 of 240 adjusted rate(s):
## Rate          : -0.06159952
## Adjustment    : -0.001461925
## Adjusted Rate : -0.06013759 
## 
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------

Converting units

mmr_adj2 <- mmr_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253)
## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

selecting rates

mmr_final <- mmr_adj2 |> 
  select_rate(method = "rsq", n=c(0.93,1)) |> 
  select_rate(method = "highest", n=1) |> 
  plot(type="full") |> 
  summary(export=TRUE)
## select_rate: Selecting rates with rsq values between 0.93 and 1...
## ----- Selection complete. 0 rate(s) removed, 240 rate(s) remaining -----
## select_rate: Selecting highest 1 *absolute* rate values...
## ----- Selection complete. 239 rate(s) removed, 1 rate(s) remaining -----
## 
## # plot.convert_rate # -------------------
## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1       rsq density row endrow    time
## 1:  NA    1     543.1568 -0.06159952 0.9971818      NA 105    165 7289.43
##    endtime    oxy endoxy        rate   adjustment rate.adjusted  rate.input
## 1: 7349.43 94.052 90.528 -0.06159952 -0.001461925   -0.06013759 -0.06013759
##    oxy.unit time.unit volume      mass area  S    t        P   rate.abs
## 1:     %Air       sec 0.0465 0.0006171   NA 36 28.5 1.013253 -0.6377325
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -1033.435          NA  mgO2/hr/kg   -1033.435
## -----------------------------------------

Results

results <-  results |> 
  mutate(Max_kg = mmr_final$rate.output*-1, 
         Max = (mmr_final$rate.output*-1)*mass, 
         rsqmax =mmr_final$rsq,
         AAS_kg = Max_kg - Resting_kg, 
         AAS = Max - Resting, 
                      Notes=Notes, 
                      True_resting="") 
knitr::kable(results, "simple") 
Clutch Replicate Male Female Population Tank Mass Chamber System Volume Date_tested Date_analysed Swim Salinity Temperature Resting_kg Resting rsqrest Max_kg Max rsqmax AAS_kg AAS Notes True_resting
108 4 CARL369 CARL349 Arlington reef 216 0.0006171 ch1 Dell 0.0465 2023-05-20 2024-06-14 good/good 36 28.5 464.0817 0.2863848 0.9962 1033.435 0.6377325 0.9971818 569.353 0.3513477

Exporting data

resp_results_juveniles <- read_csv("resp_results_juveniles.csv") 
## Rows: 73 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
resp_results_juveniles <- rbind(resp_results_juveniles, results) 
resp_results_juveniles 
write.csv(resp_results_juveniles, file="./resp_results_juveniles.csv", row.names = FALSE)

5

Enter specimen data

Replicate = 5 
mass = 0.0006830 
chamber = "ch4" 
Swim = "good/good"
chamber_vol = chamber4_asus
system1 = "Asus"
Notes=""

##--- time of trail ---## 
experiment_mmr_date_asus <- "20 May 2023 12 28PM/Oxygen"
experiment_mmr_date2_asus <- "20 May 2023 12 28PM/All"

firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date_asus,"data raw/Firesting.txt"), 
    delim = "\t", escape_double = FALSE, 
    col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"), 
        `Time (s)` = col_number(), Ch1...5 = col_number(), 
        Ch2...6 = col_number(), Ch3...7 = col_number(), 
        Ch4...8 = col_number()), trim_ws = TRUE, 
    skip = 19) 
## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`
## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
##   dat <- vroom(...)
##   problems(dat)
Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date2_asus,"slopes/Cycle_1.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 
## New names:
## • `` -> `...8`

Background rates

Pre-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",preexperiment_date_asus,"slopes")) 

pre_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

pre_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

pre_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))


bg_pre1 <- pre_cycle1 %>% calc_rate.bg()
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre2 <- pre_cycle2 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre3 <- pre_cycle3 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre <- mean(bg_pre1$rate.bg.mean,bg_pre2$rate.bg.mean,bg_pre3$rate.bg.mean) 
bg_pre
## [1] 0.001278047

post-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",postexperiment_date_asus,"slopes")) 
 

post_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

post_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

post_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

bg_post1 <- post_cycle1 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post2 <- post_cycle2 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post3 <- post_cycle3 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post <- mean(bg_post1$rate.bg.mean,bg_post2$rate.bg.mean,bg_post3$rate.bg.mean)
bg_post 
## [1] -0.003255418

Resting metabolic rate

Data manipulation

firesting2_asus <- firesting_asus |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_asus, time=1, oxygen=2)
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch4
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.32 1.61
## -----------------------------------------

#### subset data

Tstart.row=which(firesting2_asus$TIME ==Cycle_1_asus$Time[1], firesting_asus$TIME) 
Tstart.dTIME=as.numeric(firesting2_asus[Tstart.row, "dTIME"]) 

Tend.row=which(firesting2_asus$TIME ==tail(Cycle_last_asus$Time, n=1), firesting_asus$TIME) 
Tend.dTIME=as.numeric(firesting2_asus[Tend.row, "dTIME"])  

apoly_insp <- firesting2_asus |> 
  subset_data(from=Tstart.dTIME, 
              to=Tend.dTIME, 
              by="time") 

apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)
## 
## # print.inspect # -----------------------
##                 dTIME  ch4
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 14 15 16 17 18 19 20 21
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.32 1.48
## -----------------------------------------

Extract rates

apoly_cr.int <- calc_rate.int(apoly_insp, 
                              starts=(195+45+300), 
                              wait=45, 
                              measure=255, 
                              by="time", 
                              plot=TRUE) 
## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...
## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.

## -----------------------------------------

adjust rates for background

apoly_cr.int_adj <- adjust_rate(apoly_cr.int, 
                                by = bg_pre, 
                                by2 = bg_post, 
                                time_by = Tstart.row, 
                                time_by2 = Tend.row,
                                method = "linear")
## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates. 
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.
## Warning: adjust_rate: background rates in 'by' and 'by2' differ in sign (i.e. one is +ve, one is -ve). 
## Ensure this is correct. The 'linear' adjustment has been performed regardless.
## adjust_rate: Rate adjustments applied using "linear" method.
apoly_cr.int_adj$summary

Converting units

apoly_cr.int_adj2 <- apoly_cr.int_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253) 
## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.
apoly_cr.int_adj2$summary

Plot curve

ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) + 
  geom_point() + 
  stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
  theme_classic()

Rate filtering

apoly_rmr <- apoly_cr.int_adj2 |> 
  select_rate(method ="rsq", n=c(0.95,1)) |> 
  select_rate(method="lowest", n=6) |> 
  plot(type="full") |> 
  summary(export = TRUE)
## select_rate: Selecting rates with rsq values between 0.95 and 1...
## ----- Selection complete. 0 rate(s) removed, 23 rate(s) remaining -----
## select_rate: Selecting lowest 6 *absolute* rate values...
## ----- Selection complete. 17 rate(s) removed, 6 rate(s) remaining -----
## 
## # plot.convert_rate # -------------------
## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1   rsq density  row endrow     time
## 1:  15    1     343.0144 -0.01922083 0.995      NA 5627   5816 12673.21
## 2:  17    1     355.8986 -0.01860491 0.974      NA 6426   6615 13753.28
## 3:  20    1     393.7911 -0.01910534 0.977      NA 7626   7815 15374.47
## 4:  21    1     418.3711 -0.01999864 0.963      NA 8024   8213 15913.41
## 5:  22    1     433.7242 -0.02028129 0.995      NA 8423   8612 16453.60
## 6:  23    1     404.2413 -0.01791648 0.975      NA 8823   9012 16993.62
##     endtime     oxy endoxy        rate   adjustment rate.adjusted  rate.input
## 1: 12928.69  99.398 94.432 -0.01922083 -0.003280045   -0.01594079 -0.01594079
## 2: 14008.60  99.485 94.825 -0.01860491 -0.003823391   -0.01478152 -0.01478152
## 3: 15630.40  99.713 94.786 -0.01910534 -0.004639171   -0.01446617 -0.01446617
## 4: 16169.27  99.559 94.518 -0.01999864 -0.004910296   -0.01508835 -0.01508835
## 5: 16708.94 100.030 95.006 -0.02028129 -0.005181937   -0.01509935 -0.01509935
## 6: 17248.65  99.417 95.058 -0.01791648 -0.005453545   -0.01246293 -0.01246293
##    oxy.unit time.unit  volume     mass area  S    t        P   rate.abs
## 1:     %Air       sec 0.04791 0.000683   NA 36 28.5 1.013253 -0.1741709
## 2:     %Air       sec 0.04791 0.000683   NA 36 28.5 1.013253 -0.1615046
## 3:     %Air       sec 0.04791 0.000683   NA 36 28.5 1.013253 -0.1580590
## 4:     %Air       sec 0.04791 0.000683   NA 36 28.5 1.013253 -0.1648570
## 5:     %Air       sec 0.04791 0.000683   NA 36 28.5 1.013253 -0.1649772
## 6:     %Air       sec 0.04791 0.000683   NA 36 28.5 1.013253 -0.1361714
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -255.0086          NA  mgO2/hr/kg   -255.0086
## 2:   -236.4635          NA  mgO2/hr/kg   -236.4635
## 3:   -231.4188          NA  mgO2/hr/kg   -231.4188
## 4:   -241.3719          NA  mgO2/hr/kg   -241.3719
## 5:   -241.5479          NA  mgO2/hr/kg   -241.5479
## 6:   -199.3725          NA  mgO2/hr/kg   -199.3725
## -----------------------------------------
## remove lowest slope 
apoly_rmr <- apoly_rmr |> 
  filter(rate.output != max(rate.output))

Results

results <- data.frame(Clutch = Clutch, 
                      Replicate =Replicate, 
                      Male=Male, 
                      Female=Female,
                      Population = Population, 
                      Tank = Tank,
                      Mass = mass, 
                      Chamber = chamber, 
                      System = system1,
                      Volume = chamber_vol, 
                      Date_tested = Date_tested, 
                      Date_analysed =Date_analysed,
                      Swim = Swim,
                      Salinity = salinity, 
                      Temperature = as.numeric(unique(firesting2$temperature)), 
                      Resting_kg = mean(apoly_rmr$rate.output*-1), 
                      Resting =  mean(apoly_rmr$rate.output*-1)*mass, 
                      rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") 
Clutch Replicate Male Female Population Tank Mass Chamber System Volume Date_tested Date_analysed Swim Salinity Temperature Resting_kg Resting rsqrest
108 5 CARL369 CARL349 Arlington reef 216 0.000683 ch4 Asus 0.04791 2023-05-20 2024-06-14 good/good 36 28.5 241.1621 0.1647137 0.9808

Maximum oxygen consumption

Data manipulation

firesting2_mmr <- firesting_mmr |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_mmr, time=1, oxygen=2)
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch4
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.33 1.61
## -----------------------------------------

Subset data

cycle1.start <-  Cycle_1.mmr[1,1]
cycle1.end <-  tail(Cycle_1.mmr, n=1)[1,1] 

cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start
## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="
cycle1.end.row <- which(firesting2_mmr$TIME == cycle1.end); cycle1.end 
## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="
cycle1_data <- firesting2_mmr |> 
  subset_data(from = cycle1.start.row, 
              to = cycle1.end.row, 
              by = "row") 
## subset_data: Multi-column dataset detected in input! 
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively. 
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.
inspect(cycle1_data)
## inspect: Applying column default of 'time = 1'
## inspect: Applying column default of 'oxygen = 2'
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch4
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  5  6  7  9 10 11 12 13 14 15 16 17 18 19 20 21 23
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.33 1.41
## -----------------------------------------

Calculating MMR

mmr <- auto_rate(cycle1_data, method = "highest", plot=TRUE, width=60, by="time") |> 
  summary()
## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
##   If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()

## 
## # summary.auto_rate # -------------------
## 
## === Summary of Results by Highest Rate ===
##      rep rank intercept_b0    slope_b1       rsq density row endrow    time
##   1:  NA    1     483.6308 -0.08580953 0.9842746      NA  91    136 4545.16
##   2:  NA    2     483.5957 -0.08579860 0.9842428      NA  92    137 4546.53
##   3:  NA    3     482.8782 -0.08564882 0.9836746      NA  90    135 4543.81
##   4:  NA    4     482.8691 -0.08563723 0.9838076      NA  93    138 4547.88
##   5:  NA    5     481.8458 -0.08541155 0.9832750      NA  94    139 4549.21
##  ---                                                                       
## 174:  NA  174     259.2338 -0.03705888 0.9798603      NA 142    187 4614.05
## 175:  NA  175     258.7251 -0.03695125 0.9794928      NA 138    183 4608.63
## 176:  NA  176     257.8061 -0.03675191 0.9787854      NA 141    186 4612.68
## 177:  NA  177     257.4226 -0.03667021 0.9787389      NA 139    184 4609.96
## 178:  NA  178     256.9053 -0.03655817 0.9782925      NA 140    185 4611.32
##      endtime    oxy endoxy        rate
##   1: 4605.16 93.238 88.812 -0.08580953
##   2: 4606.53 93.231 88.693 -0.08579860
##   3: 4603.81 93.235 88.850 -0.08564882
##   4: 4607.88 93.226 88.562 -0.08563723
##   5: 4609.21 93.176 88.436 -0.08541155
##  ---                                  
## 174: 4674.05 88.144 86.004 -0.03705888
## 175: 4668.63 88.562 86.339 -0.03695125
## 176: 4672.68 88.232 86.064 -0.03675191
## 177: 4669.96 88.436 86.267 -0.03667021
## 178: 4671.32 88.318 86.213 -0.03655817
## 
## Regressions : 178 | Results : 178 | Method : highest | Roll width : 60 | Roll type : time 
## -----------------------------------------

Adjusting

mmr_adj <- adjust_rate(mmr, by=bg_pre, method = "mean");mmr_adj
## adjust_rate: Rate adjustments applied using "mean" method.
## 
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
## 
## Adjustment was applied using the 'mean' method.
## 
## Rank 1 of 178 adjusted rate(s):
## Rate          : -0.08580953
## Adjustment    : 0.001278047
## Adjusted Rate : -0.08708758 
## 
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------

Converting units

mmr_adj2 <- mmr_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253)
## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

selecting rates

mmr_final <- mmr_adj2 |> 
  select_rate(method = "rsq", n=c(0.93,1)) |> 
  select_rate(method = "highest", n=1) |> 
  plot(type="full") |> 
  summary(export=TRUE)
## select_rate: Selecting rates with rsq values between 0.93 and 1...
## ----- Selection complete. 13 rate(s) removed, 165 rate(s) remaining -----
## select_rate: Selecting highest 1 *absolute* rate values...
## ----- Selection complete. 164 rate(s) removed, 1 rate(s) remaining -----
## 
## # plot.convert_rate # -------------------
## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1       rsq density row endrow    time
## 1:  NA    1     483.6308 -0.08580953 0.9842746      NA  91    136 4545.16
##    endtime    oxy endoxy        rate  adjustment rate.adjusted  rate.input
## 1: 4605.16 93.238 88.812 -0.08580953 0.001278047   -0.08708758 -0.08708758
##    oxy.unit time.unit  volume     mass area  S    t        P   rate.abs
## 1:     %Air       sec 0.04791 0.000683   NA 36 28.5 1.013253 -0.9515289
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -1393.161          NA  mgO2/hr/kg   -1393.161
## -----------------------------------------

Results

results <-  results |> 
  mutate(Max_kg = mmr_final$rate.output*-1, 
         Max = (mmr_final$rate.output*-1)*mass,
         rsqmax =mmr_final$rsq,
         AAS_kg = Max_kg - Resting_kg, 
         AAS = Max - Resting, 
         Notes=Notes, 
         True_resting="") 
knitr::kable(results, "simple") 
Clutch Replicate Male Female Population Tank Mass Chamber System Volume Date_tested Date_analysed Swim Salinity Temperature Resting_kg Resting rsqrest Max_kg Max rsqmax AAS_kg AAS Notes True_resting
108 5 CARL369 CARL349 Arlington reef 216 0.000683 ch4 Asus 0.04791 2023-05-20 2024-06-14 good/good 36 28.5 241.1621 0.1647137 0.9808 1393.161 0.9515289 0.9842746 1151.999 0.7868152
### Expor ting data
resp_results_juveniles <- read_csv("resp_results_juveniles.csv") 
## Rows: 74 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
resp_results_juveniles <- rbind(resp_results_juveniles, results) 
resp_results_juveniles 
write.csv(resp_results_juveniles, file="./resp_results_juveniles.csv", row.names = FALSE)

6

Enter specimen data

Replicate = 6 
mass = 0.0003585 
chamber = "ch3" 
Swim = "good/good"
chamber_vol = chamber3_asus
system1 = "Asus"
Notes=""

##--- time of trail ---## 
experiment_mmr_date_asus <- "20 May 2023 12 00PM/Oxygen"
experiment_mmr_date2_asus <- "20 May 2023 12 00PM/All"

firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date_asus,"data raw/Firesting.txt"), 
    delim = "\t", escape_double = FALSE, 
    col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"), 
        `Time (s)` = col_number(), Ch1...5 = col_number(), 
        Ch2...6 = col_number(), Ch3...7 = col_number(), 
        Ch4...8 = col_number()), trim_ws = TRUE, 
    skip = 19) 
## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`
## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
##   dat <- vroom(...)
##   problems(dat)
Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date2_asus,"slopes/Cycle_1.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 
## New names:
## • `` -> `...8`

Background rates

Pre-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",preexperiment_date_asus,"slopes")) 

pre_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

pre_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

pre_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))


bg_pre1 <- pre_cycle1 %>% calc_rate.bg()
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre2 <- pre_cycle2 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre3 <- pre_cycle3 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre <- mean(bg_pre1$rate.bg.mean,bg_pre2$rate.bg.mean,bg_pre3$rate.bg.mean) 
bg_pre
## [1] -0.0004905135

post-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",postexperiment_date_asus,"slopes")) 
 

post_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

post_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

post_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

bg_post1 <- post_cycle1 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post2 <- post_cycle2 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post3 <- post_cycle3 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post <- mean(bg_post1$rate.bg.mean,bg_post2$rate.bg.mean,bg_post3$rate.bg.mean)
bg_post 
## [1] -0.003193216

Resting metabolic rate

Data manipulation

firesting2_asus <- firesting_asus |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_asus, time=1, oxygen=2)
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch3
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.32 1.61
## -----------------------------------------

#### subset data

Tstart.row=which(firesting2_asus$TIME ==Cycle_1_asus$Time[1], firesting_asus$TIME) 
Tstart.dTIME=as.numeric(firesting2_asus[Tstart.row, "dTIME"]) 

Tend.row=which(firesting2_asus$TIME ==tail(Cycle_last_asus$Time, n=1), firesting_asus$TIME) 
Tend.dTIME=as.numeric(firesting2_asus[Tend.row, "dTIME"])  

apoly_insp <- firesting2_asus |> 
  subset_data(from=Tstart.dTIME, 
              to=Tend.dTIME, 
              by="time") 

apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)
## 
## # print.inspect # -----------------------
##                 dTIME  ch3
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 14 15 16 17 18 19 20 21
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.32 1.48
## -----------------------------------------

Extract rates

apoly_cr.int <- calc_rate.int(apoly_insp, 
                              starts=(195+45+300), 
                              wait=45, 
                              measure=245, 
                              by="time", 
                              plot=TRUE) 
## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...
## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.

## -----------------------------------------

adjust rates for background

apoly_cr.int_adj <- adjust_rate(apoly_cr.int, 
                                by = bg_pre, 
                                by2 = bg_post, 
                                time_by = Tstart.row, 
                                time_by2 = Tend.row,
                                method = "linear")
## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates. 
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.
## adjust_rate: Rate adjustments applied using "linear" method.
apoly_cr.int_adj$summary

Converting units

apoly_cr.int_adj2 <- apoly_cr.int_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253) 
## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.
apoly_cr.int_adj2$summary

Plot curve

ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) + 
  geom_point() + 
  stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
  theme_classic()

Rate filtering

apoly_rmr <- apoly_cr.int_adj2 |> 
  select_rate(method ="rsq", n=c(0.95,1)) |> 
  select_rate(method="lowest", n=6) |> 
  plot(type="full") |> 
  summary(export = TRUE)
## select_rate: Selecting rates with rsq values between 0.95 and 1...
## ----- Selection complete. 1 rate(s) removed, 22 rate(s) remaining -----
## select_rate: Selecting lowest 6 *absolute* rate values...
## ----- Selection complete. 16 rate(s) removed, 6 rate(s) remaining -----
## 
## # plot.convert_rate # -------------------
## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1   rsq density  row endrow     time
## 1:  10    1     277.4103 -0.01808389 0.951      NA 3630   3812  9973.64
## 2:  13    1     270.0228 -0.01489603 0.994      NA 4829   5010 11594.39
## 3:  14    1     315.3691 -0.01791020 0.988      NA 5228   5409 12134.17
## 4:  15    1     329.7574 -0.01827326 0.993      NA 5627   5809 12673.21
## 5:  17    1     355.1076 -0.01866745 0.996      NA 6426   6608 13753.28
## 6:  23    1     434.4844 -0.01980479 0.997      NA 8823   9005 16993.62
##     endtime    oxy endoxy        rate   adjustment rate.adjusted  rate.input
## 1: 10219.61 97.246 93.601 -0.01808389 -0.002396780   -0.01568711 -0.01568711
## 2: 11839.13 97.254 93.516 -0.01489603 -0.002882713   -0.01201331 -0.01201331
## 3: 12378.91 97.798 93.608 -0.01791020 -0.003044611   -0.01486559 -0.01486559
## 4: 12919.26 98.056 93.471 -0.01827326 -0.003206484   -0.01506678 -0.01506678
## 5: 13999.15 98.345 93.795 -0.01866745 -0.003530407   -0.01513704 -0.01513704
## 6: 17239.24 97.736 93.165 -0.01980479 -0.004502256   -0.01530253 -0.01530253
##    oxy.unit time.unit  volume      mass area  S    t        P   rate.abs
## 1:     %Air       sec 0.04551 0.0003585   NA 36 28.5 1.013253 -0.1628131
## 2:     %Air       sec 0.04551 0.0003585   NA 36 28.5 1.013253 -0.1246836
## 3:     %Air       sec 0.04551 0.0003585   NA 36 28.5 1.013253 -0.1542868
## 4:     %Air       sec 0.04551 0.0003585   NA 36 28.5 1.013253 -0.1563748
## 5:     %Air       sec 0.04551 0.0003585   NA 36 28.5 1.013253 -0.1571041
## 6:     %Air       sec 0.04551 0.0003585   NA 36 28.5 1.013253 -0.1588217
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -454.1510          NA  mgO2/hr/kg   -454.1510
## 2:   -347.7924          NA  mgO2/hr/kg   -347.7924
## 3:   -430.3675          NA  mgO2/hr/kg   -430.3675
## 4:   -436.1920          NA  mgO2/hr/kg   -436.1920
## 5:   -438.2262          NA  mgO2/hr/kg   -438.2262
## 6:   -443.0172          NA  mgO2/hr/kg   -443.0172
## -----------------------------------------
## remove lowest slope 
apoly_rmr <- apoly_rmr |> 
  filter(rate.output != max(rate.output))

Results

results <- data.frame(Clutch = Clutch, 
                      Replicate =Replicate, 
                      Male=Male, 
                      Female=Female,
                      Population = Population, 
                      Tank = Tank,
                      Mass = mass, 
                      Chamber = chamber, 
                      System = system1,
                      Volume = chamber_vol, 
                      Date_tested = Date_tested, 
                      Date_analysed =Date_analysed,
                      Swim = Swim,
                      Salinity = salinity, 
                      Temperature = as.numeric(unique(firesting2$temperature)), 
                      Resting_kg = mean(apoly_rmr$rate.output*-1), 
                      Resting =  mean(apoly_rmr$rate.output*-1)*mass, 
                      rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") 
Clutch Replicate Male Female Population Tank Mass Chamber System Volume Date_tested Date_analysed Swim Salinity Temperature Resting_kg Resting rsqrest
108 6 CARL369 CARL349 Arlington reef 216 0.0003585 ch3 Asus 0.04551 2023-05-20 2024-06-14 good/good 36 28.5 440.3908 0.1578801 0.985

Maximum oxygen consumption

Data manipulation

firesting2_mmr <- firesting_mmr |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_mmr, time=1, oxygen=2)
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch3
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.33 1.61
## -----------------------------------------

Subset data

cycle1.start <-  Cycle_1.mmr[1,1]
cycle1.end <-  tail(Cycle_1.mmr, n=1)[1,1] 

cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start
## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="
cycle1.end.row <- which(firesting2_mmr$TIME == cycle1.end); cycle1.end 
## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="
cycle1_data <- firesting2_mmr |> 
  subset_data(from = cycle1.start.row, 
              to = cycle1.end.row, 
              by = "row") 
## subset_data: Multi-column dataset detected in input! 
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively. 
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.
inspect(cycle1_data)
## inspect: Applying column default of 'time = 1'
## inspect: Applying column default of 'oxygen = 2'
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch3
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  2  3  5  6  7  8 10 11 12 13 15 16 17 18 21 22 23 24 25 26
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.33 1.53
## -----------------------------------------

Calculating MMR

mmr <- auto_rate(cycle1_data, method = "highest", plot=TRUE, width=60, by="time") |> 
  summary()
## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
##   If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()

## 
## # summary.auto_rate # -------------------
## 
## === Summary of Results by Highest Rate ===
##      rep rank intercept_b0    slope_b1       rsq density row endrow    time
##   1:  NA    1     286.3269 -0.06725407 0.9732502      NA  90    135 2844.54
##   2:  NA    2     286.1929 -0.06721230 0.9730453      NA  89    134 2843.17
##   3:  NA    3     286.0705 -0.06715980 0.9727589      NA  91    136 2845.94
##   4:  NA    4     285.2236 -0.06687986 0.9713128      NA  88    133 2841.83
##   5:  NA    5     285.0128 -0.06678723 0.9708498      NA  92    137 2847.28
##  ---                                                                       
## 173:  NA  173     166.2904 -0.02496298 0.9041856      NA   5     50 2729.16
## 174:  NA  174     163.6473 -0.02400257 0.9101347      NA   4     49 2727.81
## 175:  NA  175     161.2611 -0.02313501 0.9158004      NA   3     48 2726.48
## 176:  NA  176     158.8830 -0.02226997 0.9227505      NA   2     47 2725.14
## 177:  NA  177     157.1546 -0.02163984 0.9311251      NA   1     46 2723.79
##      endtime    oxy endoxy        rate
##   1: 2904.54 94.771 91.283 -0.06725407
##   2: 2903.17 94.767 91.308 -0.06721230
##   3: 2905.94 94.704 91.252 -0.06715980
##   4: 2901.83 94.828 91.330 -0.06687986
##   5: 2907.28 94.699 91.231 -0.06678723
##  ---                                  
## 173: 2789.16 98.097 96.318 -0.02496298
## 174: 2787.81 98.119 96.416 -0.02400257
## 175: 2786.48 98.131 96.479 -0.02313501
## 176: 2785.14 98.230 96.561 -0.02226997
## 177: 2783.79 98.340 96.612 -0.02163984
## 
## Regressions : 177 | Results : 177 | Method : highest | Roll width : 60 | Roll type : time 
## -----------------------------------------

Adjusting

mmr_adj <- adjust_rate(mmr, by=bg_pre, method = "mean");mmr_adj
## adjust_rate: Rate adjustments applied using "mean" method.
## 
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
## 
## Adjustment was applied using the 'mean' method.
## 
## Rank 1 of 177 adjusted rate(s):
## Rate          : -0.06725407
## Adjustment    : -0.0004905135
## Adjusted Rate : -0.06676356 
## 
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------

Converting units

mmr_adj2 <- mmr_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253)
## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

selecting rates

mmr_final <- mmr_adj2 |> 
  select_rate(method = "rsq", n=c(0.93,1)) |> 
  select_rate(method = "highest", n=1) |> 
  plot(type="full") |> 
  summary(export=TRUE)
## select_rate: Selecting rates with rsq values between 0.93 and 1...
## ----- Selection complete. 31 rate(s) removed, 146 rate(s) remaining -----
## select_rate: Selecting highest 1 *absolute* rate values...
## ----- Selection complete. 145 rate(s) removed, 1 rate(s) remaining -----
## 
## # plot.convert_rate # -------------------
## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1       rsq density row endrow    time
## 1:  NA    1     286.3269 -0.06725407 0.9732502      NA  90    135 2844.54
##    endtime    oxy endoxy        rate    adjustment rate.adjusted  rate.input
## 1: 2904.54 94.771 91.283 -0.06725407 -0.0004905135   -0.06676356 -0.06676356
##    oxy.unit time.unit  volume      mass area  S    t        P   rate.abs
## 1:     %Air       sec 0.04551 0.0003585   NA 36 28.5 1.013253 -0.6929245
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -1932.844          NA  mgO2/hr/kg   -1932.844
## -----------------------------------------

Results

results <-  results |> 
  mutate(Max_kg = mmr_final$rate.output*-1, 
         Max = (mmr_final$rate.output*-1)*mass,
         rsqmax =mmr_final$rsq,
         AAS_kg = Max_kg - Resting_kg, 
         AAS = Max - Resting, 
         Notes=Notes, 
         True_resting="") 
knitr::kable(results, "simple") 
Clutch Replicate Male Female Population Tank Mass Chamber System Volume Date_tested Date_analysed Swim Salinity Temperature Resting_kg Resting rsqrest Max_kg Max rsqmax AAS_kg AAS Notes True_resting
108 6 CARL369 CARL349 Arlington reef 216 0.0003585 ch3 Asus 0.04551 2023-05-20 2024-06-14 good/good 36 28.5 440.3908 0.1578801 0.985 1932.844 0.6929245 0.9732502 1492.453 0.5350444
### Expor ting data
resp_results_juveniles <- read_csv("resp_results_juveniles.csv") 
## Rows: 75 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
resp_results_juveniles <- rbind(resp_results_juveniles, results) 
resp_results_juveniles 
write.csv(resp_results_juveniles, file="./resp_results_juveniles.csv", row.names = FALSE)

7

Enter specimen data

Replicate = 7 
mass = 0.0006260 
chamber = "ch2" 
Swim = "good/good"
chamber_vol = chamber2_asus
system1 = "Asus"
Notes=""

##--- time of trail ---## 
experiment_mmr_date_asus <- "20 May 2023 12 39PM/Oxygen"
experiment_mmr_date2_asus <- "20 May 2023 12 39PM/All"

firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date_asus,"data raw/Firesting.txt"), 
    delim = "\t", escape_double = FALSE, 
    col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"), 
        `Time (s)` = col_number(), Ch1...5 = col_number(), 
        Ch2...6 = col_number(), Ch3...7 = col_number(), 
        Ch4...8 = col_number()), trim_ws = TRUE, 
    skip = 19) 
## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`
## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
##   dat <- vroom(...)
##   problems(dat)
Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date2_asus,"slopes/Cycle_1.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 
## New names:
## • `` -> `...8`

Background rates

Pre-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",preexperiment_date_asus,"slopes")) 

pre_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

pre_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

pre_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))


bg_pre1 <- pre_cycle1 %>% calc_rate.bg()
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre2 <- pre_cycle2 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre3 <- pre_cycle3 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre <- mean(bg_pre1$rate.bg.mean,bg_pre2$rate.bg.mean,bg_pre3$rate.bg.mean) 
bg_pre
## [1] -0.0005649641

post-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",postexperiment_date_asus,"slopes")) 
 

post_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

post_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

post_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

bg_post1 <- post_cycle1 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post2 <- post_cycle2 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post3 <- post_cycle3 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post <- mean(bg_post1$rate.bg.mean,bg_post2$rate.bg.mean,bg_post3$rate.bg.mean)
bg_post 
## [1] -0.001623029

Resting metabolic rate

Data manipulation

firesting2_asus <- firesting_asus |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_asus, time=1, oxygen=2)
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch2
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.32 1.61
## -----------------------------------------

#### subset data

Tstart.row=which(firesting2_asus$TIME ==Cycle_1_asus$Time[1], firesting_asus$TIME) 
Tstart.dTIME=as.numeric(firesting2_asus[Tstart.row, "dTIME"]) 

Tend.row=which(firesting2_asus$TIME ==tail(Cycle_last_asus$Time, n=1), firesting_asus$TIME) 
Tend.dTIME=as.numeric(firesting2_asus[Tend.row, "dTIME"])  

apoly_insp <- firesting2_asus |> 
  subset_data(from=Tstart.dTIME, 
              to=Tend.dTIME, 
              by="time") 

apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)
## 
## # print.inspect # -----------------------
##                 dTIME  ch2
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 14 15 16 17 18 19 20 21
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.32 1.48
## -----------------------------------------

Extract rates

apoly_cr.int <- calc_rate.int(apoly_insp, 
                              starts=(195+45+300), 
                              wait=45, 
                              measure=245, 
                              by="time", 
                              plot=TRUE) 
## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...
## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.

## -----------------------------------------

adjust rates for background

apoly_cr.int_adj <- adjust_rate(apoly_cr.int, 
                                by = bg_pre, 
                                by2 = bg_post, 
                                time_by = Tstart.row, 
                                time_by2 = Tend.row,
                                method = "linear")
## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates. 
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.
## adjust_rate: Rate adjustments applied using "linear" method.
apoly_cr.int_adj$summary

Converting units

apoly_cr.int_adj2 <- apoly_cr.int_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253) 
## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.
apoly_cr.int_adj2$summary

Plot curve

ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) + 
  geom_point() + 
  stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
  theme_classic()

Rate filtering

apoly_rmr <- apoly_cr.int_adj2 |> 
  select_rate(method ="rsq", n=c(0.95,1)) |> 
  select_rate(method="lowest", n=6) |> 
  plot(type="full") |> 
  summary(export = TRUE)
## select_rate: Selecting rates with rsq values between 0.95 and 1...
## ----- Selection complete. 1 rate(s) removed, 22 rate(s) remaining -----
## select_rate: Selecting lowest 6 *absolute* rate values...
## ----- Selection complete. 16 rate(s) removed, 6 rate(s) remaining -----
## 
## # plot.convert_rate # -------------------
## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1   rsq density  row endrow     time
## 1:  10    1     368.8054 -0.02714461 0.996      NA 3630   3812  9973.64
## 2:  14    1     452.8558 -0.02916966 0.995      NA 5228   5409 12134.17
## 3:  17    1     502.5088 -0.02933535 0.989      NA 6426   6608 13753.28
## 4:  19    1     524.6337 -0.02866718 0.989      NA 7226   7407 14833.66
## 5:  20    1     531.6246 -0.02812227 0.994      NA 7626   7807 15374.47
## 6:  22    1     584.9332 -0.02950285 0.992      NA 8423   8604 16453.60
##     endtime    oxy endoxy        rate   adjustment rate.adjusted  rate.input
## 1: 10219.61 97.890 91.115 -0.02714461 -0.001311237   -0.02583337 -0.02583337
## 2: 12378.91 98.645 91.373 -0.02916966 -0.001564852   -0.02760481 -0.02760481
## 3: 13999.15 99.051 91.388 -0.02933535 -0.001755033   -0.02758032 -0.02758032
## 4: 15078.47 99.218 92.300 -0.02866718 -0.001881829   -0.02678536 -0.02678536
## 5: 15619.56 99.002 92.520 -0.02812227 -0.001945347   -0.02617692 -0.02617692
## 6: 16698.19 99.411 92.117 -0.02950285 -0.002072028   -0.02743082 -0.02743082
##    oxy.unit time.unit  volume     mass area  S    t        P   rate.abs
## 1:     %Air       sec 0.04573 0.000626   NA 36 28.5 1.013253 -0.2694150
## 2:     %Air       sec 0.04573 0.000626   NA 36 28.5 1.013253 -0.2878893
## 3:     %Air       sec 0.04573 0.000626   NA 36 28.5 1.013253 -0.2876339
## 4:     %Air       sec 0.04573 0.000626   NA 36 28.5 1.013253 -0.2793433
## 5:     %Air       sec 0.04573 0.000626   NA 36 28.5 1.013253 -0.2729979
## 6:     %Air       sec 0.04573 0.000626   NA 36 28.5 1.013253 -0.2860748
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -430.3755          NA  mgO2/hr/kg   -430.3755
## 2:   -459.8871          NA  mgO2/hr/kg   -459.8871
## 3:   -459.4790          NA  mgO2/hr/kg   -459.4790
## 4:   -446.2353          NA  mgO2/hr/kg   -446.2353
## 5:   -436.0989          NA  mgO2/hr/kg   -436.0989
## 6:   -456.9885          NA  mgO2/hr/kg   -456.9885
## -----------------------------------------
## remove lowest slope 
apoly_rmr <- apoly_rmr |> 
  filter(rate.output != max(rate.output))

Results

results <- data.frame(Clutch = Clutch, 
                      Replicate =Replicate, 
                      Male=Male, 
                      Female=Female,
                      Population = Population, 
                      Tank = Tank,
                      Mass = mass, 
                      Chamber = chamber, 
                      System = system1,
                      Volume = chamber_vol, 
                      Date_tested = Date_tested, 
                      Date_analysed =Date_analysed,
                      Swim = Swim,
                      Salinity = salinity, 
                      Temperature = as.numeric(unique(firesting2$temperature)), 
                      Resting_kg = mean(apoly_rmr$rate.output*-1), 
                      Resting =  mean(apoly_rmr$rate.output*-1)*mass, 
                      rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") 
Clutch Replicate Male Female Population Tank Mass Chamber System Volume Date_tested Date_analysed Swim Salinity Temperature Resting_kg Resting rsqrest
108 7 CARL369 CARL349 Arlington reef 216 0.000626 ch2 Asus 0.04573 2023-05-20 2024-06-14 good/good 36 28.5 451.7378 0.2827878 0.9918

Maximum oxygen consumption

Data manipulation

firesting2_mmr <- firesting_mmr |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_mmr, time=1, oxygen=2)
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch2
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.32 1.61
## -----------------------------------------

Subset data

cycle1.start <-  Cycle_1.mmr[1,1]
cycle1.end <-  tail(Cycle_1.mmr, n=1)[1,1] 

cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start
## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="
cycle1.end.row <- which(firesting2_mmr$TIME == cycle1.end); cycle1.end 
## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="
cycle1_data <- firesting2_mmr |> 
  subset_data(from = cycle1.start.row, 
              to = 3850, # custom 
              by = "row") 
## subset_data: Multi-column dataset detected in input! 
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively. 
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.
inspect(cycle1_data)
## inspect: Applying column default of 'time = 1'
## inspect: Applying column default of 'oxygen = 2'
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch2
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  5  8  9 10 11 12 13 15 16 18 19 20 21 23 24 25 26
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.33 1.47
## -----------------------------------------

Calculating MMR

mmr <- auto_rate(cycle1_data, method = "highest", plot=TRUE, width=60, by="time") |> 
  summary()
## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
##   If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()

## 
## # summary.auto_rate # -------------------
## 
## === Summary of Results by Highest Rate ===
##     rep rank intercept_b0    slope_b1       rsq density row endrow    time
##  1:  NA    1     454.8246 -0.06986712 0.9953478      NA  42     87 5125.25
##  2:  NA    2     454.6431 -0.06983264 0.9953065      NA  41     86 5123.91
##  3:  NA    3     454.3738 -0.06977901 0.9952410      NA  43     88 5126.60
##  4:  NA    4     453.3168 -0.06957334 0.9949731      NA  44     89 5127.96
##  5:  NA    5     453.2165 -0.06955649 0.9949826      NA  40     85 5122.58
## ---                                                                       
## 61:  NA   61     320.5558 -0.04370494 0.9867232      NA   5     50 5075.40
## 62:  NA   62     318.5896 -0.04331877 0.9880153      NA   4     49 5074.05
## 63:  NA   63     317.5516 -0.04311254 0.9888725      NA   1     46 5069.95
## 64:  NA   64     317.2871 -0.04306246 0.9889995      NA   3     48 5072.68
## 65:  NA   65     317.0575 -0.04301653 0.9891446      NA   2     47 5071.28
##     endtime    oxy endoxy        rate
##  1: 5185.25 96.694 92.622 -0.06986712
##  2: 5183.91 96.731 92.654 -0.06983264
##  3: 5186.60 96.635 92.554 -0.06977901
##  4: 5187.96 96.559 92.501 -0.06957334
##  5: 5182.58 96.730 92.657 -0.06955649
## ---                                  
## 61: 5135.40 98.794 95.918 -0.04370494
## 62: 5134.05 98.889 96.010 -0.04331877
## 63: 5129.95 99.109 96.365 -0.04311254
## 64: 5132.68 98.976 96.147 -0.04306246
## 65: 5131.28 99.063 96.253 -0.04301653
## 
## Regressions : 65 | Results : 65 | Method : highest | Roll width : 60 | Roll type : time 
## -----------------------------------------

Adjusting

mmr_adj <- adjust_rate(mmr, by=bg_pre, method = "mean");mmr_adj
## adjust_rate: Rate adjustments applied using "mean" method.
## 
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
## 
## Adjustment was applied using the 'mean' method.
## 
## Rank 1 of 65 adjusted rate(s):
## Rate          : -0.06986712
## Adjustment    : -0.0005649641
## Adjusted Rate : -0.06930215 
## 
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------

Converting units

mmr_adj2 <- mmr_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253)
## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

selecting rates

mmr_final <- mmr_adj2 |> 
  select_rate(method = "rsq", n=c(0.93,1)) |> 
  select_rate(method = "highest", n=1) |> 
  plot(type="full") |> 
  summary(export=TRUE)
## select_rate: Selecting rates with rsq values between 0.93 and 1...
## ----- Selection complete. 0 rate(s) removed, 65 rate(s) remaining -----
## select_rate: Selecting highest 1 *absolute* rate values...
## ----- Selection complete. 64 rate(s) removed, 1 rate(s) remaining -----
## 
## # plot.convert_rate # -------------------
## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1       rsq density row endrow    time
## 1:  NA    1     454.8246 -0.06986712 0.9953478      NA  42     87 5125.25
##    endtime    oxy endoxy        rate    adjustment rate.adjusted  rate.input
## 1: 5185.25 96.694 92.622 -0.06986712 -0.0005649641   -0.06930215 -0.06930215
##    oxy.unit time.unit  volume     mass area  S    t        P  rate.abs
## 1:     %Air       sec 0.04573 0.000626   NA 36 28.5 1.013253 -0.722749
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -1154.551          NA  mgO2/hr/kg   -1154.551
## -----------------------------------------

Results

results <-  results |> 
  mutate(Max_kg = mmr_final$rate.output*-1, 
         Max = (mmr_final$rate.output*-1)*mass,
         rsqmax =mmr_final$rsq,
         AAS_kg = Max_kg - Resting_kg, 
         AAS = Max - Resting, 
         Notes=Notes, 
         True_resting="") 
knitr::kable(results, "simple") 
Clutch Replicate Male Female Population Tank Mass Chamber System Volume Date_tested Date_analysed Swim Salinity Temperature Resting_kg Resting rsqrest Max_kg Max rsqmax AAS_kg AAS Notes True_resting
108 7 CARL369 CARL349 Arlington reef 216 0.000626 ch2 Asus 0.04573 2023-05-20 2024-06-14 good/good 36 28.5 451.7378 0.2827878 0.9918 1154.551 0.722749 0.9953478 702.8134 0.4399612
### Expor ting data
resp_results_juveniles <- read_csv("resp_results_juveniles.csv") 
## Rows: 76 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
resp_results_juveniles <- rbind(resp_results_juveniles, results) 
resp_results_juveniles 
write.csv(resp_results_juveniles, file="./resp_results_juveniles.csv", row.names = FALSE)

8

Enter specimen data

Replicate = 8 
mass = 0.0008961 
chamber = "ch1" 
Swim = "good/good"
chamber_vol = chamber1_asus
system1 = "Asus"
Notes="check max"

##--- time of trail ---## 
experiment_mmr_date_asus <- "20 May 2023 12 28PM/Oxygen"
experiment_mmr_date2_asus <- "20 May 2023 12 28PM/All"

firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date_asus,"data raw/Firesting.txt"), 
    delim = "\t", escape_double = FALSE, 
    col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"), 
        `Time (s)` = col_number(), Ch1...5 = col_number(), 
        Ch2...6 = col_number(), Ch3...7 = col_number(), 
        Ch4...8 = col_number()), trim_ws = TRUE, 
    skip = 19) 
## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`
## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
##   dat <- vroom(...)
##   problems(dat)
Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date2_asus,"slopes/Cycle_1.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 
## New names:
## • `` -> `...8`

Background rates

Pre-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",preexperiment_date_asus,"slopes")) 

pre_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

pre_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

pre_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))


bg_pre1 <- pre_cycle1 %>% calc_rate.bg()
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre2 <- pre_cycle2 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre3 <- pre_cycle3 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre <- mean(bg_pre1$rate.bg.mean,bg_pre2$rate.bg.mean,bg_pre3$rate.bg.mean) 
bg_pre
## [1] -0.001223244

post-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",postexperiment_date_asus,"slopes")) 
 

post_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

post_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

post_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

bg_post1 <- post_cycle1 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post2 <- post_cycle2 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post3 <- post_cycle3 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post <- mean(bg_post1$rate.bg.mean,bg_post2$rate.bg.mean,bg_post3$rate.bg.mean)
bg_post 
## [1] -0.002958744

Resting metabolic rate

Data manipulation

firesting2_asus <- firesting_asus |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_asus, time=1, oxygen=2)
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch1
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.32 1.61
## -----------------------------------------

#### subset data

Tstart.row=which(firesting2_asus$TIME ==Cycle_1_asus$Time[1], firesting_asus$TIME) 
Tstart.dTIME=as.numeric(firesting2_asus[Tstart.row, "dTIME"]) 

Tend.row=which(firesting2_asus$TIME ==tail(Cycle_last_asus$Time, n=1), firesting_asus$TIME) 
Tend.dTIME=as.numeric(firesting2_asus[Tend.row, "dTIME"])  

apoly_insp <- firesting2_asus |> 
  subset_data(from=Tstart.dTIME, 
              to=Tend.dTIME, 
              by="time") 

apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)
## 
## # print.inspect # -----------------------
##                 dTIME  ch1
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 14 15 16 17 18 19 20 21
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.32 1.48
## -----------------------------------------

Extract rates

apoly_cr.int <- calc_rate.int(apoly_insp, 
                              starts=(195+45+300), 
                              wait=45, 
                              measure=245, 
                              by="time", 
                              plot=TRUE) 
## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...
## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.

## -----------------------------------------

adjust rates for background

apoly_cr.int_adj <- adjust_rate(apoly_cr.int, 
                                by = bg_pre, 
                                by2 = bg_post, 
                                time_by = Tstart.row, 
                                time_by2 = Tend.row,
                                method = "linear")
## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates. 
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.
## adjust_rate: Rate adjustments applied using "linear" method.
apoly_cr.int_adj$summary

Converting units

apoly_cr.int_adj2 <- apoly_cr.int_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253) 
## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.
apoly_cr.int_adj2$summary

Plot curve

ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) + 
  geom_point() + 
  stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
  theme_classic()

Rate filtering

apoly_rmr <- apoly_cr.int_adj2 |> 
  select_rate(method ="rsq", n=c(0.95,1)) |> 
  select_rate(method="lowest", n=6) |> 
  plot(type="full") |> 
  summary(export = TRUE)
## select_rate: Selecting rates with rsq values between 0.95 and 1...
## ----- Selection complete. 0 rate(s) removed, 23 rate(s) remaining -----
## select_rate: Selecting lowest 6 *absolute* rate values...
## ----- Selection complete. 17 rate(s) removed, 6 rate(s) remaining -----
## 
## # plot.convert_rate # -------------------
## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1   rsq density  row endrow     time
## 1:  15    1     455.0559 -0.02816935 0.996      NA 5627   5809 12673.21
## 2:  17    1     466.6423 -0.02676325 0.986      NA 6426   6608 13753.28
## 3:  20    1     520.1748 -0.02743045 0.990      NA 7626   7807 15374.47
## 4:  21    1     504.4751 -0.02549459 0.986      NA 8024   8205 15913.41
## 5:  22    1     532.8571 -0.02637297 0.990      NA 8423   8604 16453.60
## 6:  23    1     575.0030 -0.02803572 0.996      NA 8823   9005 16993.62
##     endtime    oxy endoxy        rate   adjustment rate.adjusted  rate.input
## 1: 12919.26 97.818 90.859 -0.02816935 -0.002967264   -0.02520208 -0.02520208
## 2: 13999.15 98.262 91.378 -0.02676325 -0.003175266   -0.02358798 -0.02358798
## 3: 15619.56 98.043 91.505 -0.02743045 -0.003487428   -0.02394302 -0.02394302
## 4: 16158.48 98.379 91.798 -0.02549459 -0.003591225   -0.02190336 -0.02190336
## 5: 16698.19 98.522 92.027 -0.02637297 -0.003695218   -0.02267775 -0.02267775
## 6: 17239.24 98.199 91.558 -0.02803572 -0.003799324   -0.02423639 -0.02423639
##    oxy.unit time.unit  volume      mass area  S    t        P   rate.abs
## 1:     %Air       sec 0.04565 0.0008961   NA 36 28.5 1.013253 -0.2623716
## 2:     %Air       sec 0.04565 0.0008961   NA 36 28.5 1.013253 -0.2455677
## 3:     %Air       sec 0.04565 0.0008961   NA 36 28.5 1.013253 -0.2492638
## 4:     %Air       sec 0.04565 0.0008961   NA 36 28.5 1.013253 -0.2280296
## 5:     %Air       sec 0.04565 0.0008961   NA 36 28.5 1.013253 -0.2360915
## 6:     %Air       sec 0.04565 0.0008961   NA 36 28.5 1.013253 -0.2523181
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -292.7928          NA  mgO2/hr/kg   -292.7928
## 2:   -274.0405          NA  mgO2/hr/kg   -274.0405
## 3:   -278.1652          NA  mgO2/hr/kg   -278.1652
## 4:   -254.4689          NA  mgO2/hr/kg   -254.4689
## 5:   -263.4655          NA  mgO2/hr/kg   -263.4655
## 6:   -281.5736          NA  mgO2/hr/kg   -281.5736
## -----------------------------------------
## remove lowest slope 
apoly_rmr <- apoly_rmr |> 
  filter(rate.output != max(rate.output))

Results

results <- data.frame(Clutch = Clutch, 
                      Replicate =Replicate, 
                      Male=Male, 
                      Female=Female,
                      Population = Population, 
                      Tank = Tank,
                      Mass = mass, 
                      Chamber = chamber, 
                      System = system1,
                      Volume = chamber_vol, 
                      Date_tested = Date_tested, 
                      Date_analysed =Date_analysed,
                      Swim = Swim,
                      Salinity = salinity, 
                      Temperature = as.numeric(unique(firesting2$temperature)), 
                      Resting_kg = mean(apoly_rmr$rate.output*-1), 
                      Resting =  mean(apoly_rmr$rate.output*-1)*mass, 
                      rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") 
Clutch Replicate Male Female Population Tank Mass Chamber System Volume Date_tested Date_analysed Swim Salinity Temperature Resting_kg Resting rsqrest
108 8 CARL369 CARL349 Arlington reef 216 0.0008961 ch1 Asus 0.04565 2023-05-20 2024-06-14 good/good 36 28.5 278.0075 0.2491225 0.9916

Maximum oxygen consumption

Data manipulation

firesting2_mmr <- firesting_mmr |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_mmr, time=1, oxygen=2)
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch1
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.33 1.61
## -----------------------------------------

Subset data

cycle1.start <-  Cycle_1.mmr[1,1]
cycle1.end <-  tail(Cycle_1.mmr, n=1)[1,1] 

cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start
## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="
cycle1.end.row <- which(firesting2_mmr$TIME == cycle1.end); cycle1.end 
## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="
cycle1_data <- firesting2_mmr |> 
  subset_data(from = cycle1.start.row, 
              to = cycle1.end.row, 
              by = "row") 
## subset_data: Multi-column dataset detected in input! 
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively. 
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.
inspect(cycle1_data)
## inspect: Applying column default of 'time = 1'
## inspect: Applying column default of 'oxygen = 2'
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch1
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  5  6  7  9 10 11 12 13 14 15 16 17 18 19 20 21 23
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.33 1.41
## -----------------------------------------

Calculating MMR

mmr <- auto_rate(cycle1_data, method = "highest", plot=TRUE, width=60, by="time") |> 
  summary()
## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
##   If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()

## 
## # summary.auto_rate # -------------------
## 
## === Summary of Results by Highest Rate ===
##      rep rank intercept_b0    slope_b1       rsq density row endrow    time
##   1:  NA    1     460.4408 -0.07997673 0.9975943      NA 176    221 4659.95
##   2:  NA    2     459.9052 -0.07986288 0.9975414      NA 175    220 4658.59
##   3:  NA    3     459.6918 -0.07981684 0.9975459      NA 177    222 4661.31
##   4:  NA    4     459.6696 -0.07981290 0.9975242      NA 174    219 4657.24
##   5:  NA    5     458.8989 -0.07964902 0.9974142      NA 173    218 4655.91
##  ---                                                                       
## 174:  NA  174     264.7597 -0.03728693 0.9695487      NA   5     50 4428.96
## 175:  NA  175     260.0363 -0.03622828 0.9641469      NA   4     49 4427.62
## 176:  NA  176     254.3808 -0.03496032 0.9561719      NA   3     48 4426.28
## 177:  NA  177     249.4883 -0.03386287 0.9502761      NA   2     47 4424.94
## 178:  NA  178     245.2923 -0.03292102 0.9470517      NA   1     46 4423.57
##      endtime    oxy endoxy        rate
##   1: 4719.95 87.746 82.969 -0.07997673
##   2: 4718.59 87.878 83.082 -0.07986288
##   3: 4721.31 87.717 82.909 -0.07981684
##   4: 4717.24 87.922 83.196 -0.07981290
##   5: 4715.91 87.988 83.347 -0.07964902
##  ---                                  
## 174: 4488.96 99.314 97.227 -0.03728693
## 175: 4487.62 99.263 97.272 -0.03622828
## 176: 4486.28 99.341 97.359 -0.03496032
## 177: 4484.94 99.436 97.424 -0.03386287
## 178: 4483.57 99.488 97.479 -0.03292102
## 
## Regressions : 178 | Results : 178 | Method : highest | Roll width : 60 | Roll type : time 
## -----------------------------------------

Adjusting

mmr_adj <- adjust_rate(mmr, by=bg_pre, method = "mean");mmr_adj
## adjust_rate: Rate adjustments applied using "mean" method.
## 
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
## 
## Adjustment was applied using the 'mean' method.
## 
## Rank 1 of 178 adjusted rate(s):
## Rate          : -0.07997673
## Adjustment    : -0.001223244
## Adjusted Rate : -0.07875348 
## 
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------

Converting units

mmr_adj2 <- mmr_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253)
## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

selecting rates

mmr_final <- mmr_adj2 |> 
  select_rate(method = "rsq", n=c(0.93,1)) |> 
  select_rate(method = "highest", n=1) |> 
  plot(type="full") |> 
  summary(export=TRUE)
## select_rate: Selecting rates with rsq values between 0.93 and 1...
## ----- Selection complete. 0 rate(s) removed, 178 rate(s) remaining -----
## select_rate: Selecting highest 1 *absolute* rate values...
## ----- Selection complete. 177 rate(s) removed, 1 rate(s) remaining -----
## 
## # plot.convert_rate # -------------------
## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1       rsq density row endrow    time
## 1:  NA    1     460.4408 -0.07997673 0.9975943      NA 176    221 4659.95
##    endtime    oxy endoxy        rate   adjustment rate.adjusted  rate.input
## 1: 4719.95 87.746 82.969 -0.07997673 -0.001223244   -0.07875348 -0.07875348
##    oxy.unit time.unit  volume      mass area  S    t        P   rate.abs
## 1:     %Air       sec 0.04565 0.0008961   NA 36 28.5 1.013253 -0.8198797
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -914.9422          NA  mgO2/hr/kg   -914.9422
## -----------------------------------------

Results

results <-  results |> 
  mutate(Max_kg = mmr_final$rate.output*-1, 
         Max = (mmr_final$rate.output*-1)*mass,
         rsqmax =mmr_final$rsq,
         AAS_kg = Max_kg - Resting_kg, 
         AAS = Max - Resting, 
         Notes=Notes, 
         True_resting="") 
knitr::kable(results, "simple") 
Clutch Replicate Male Female Population Tank Mass Chamber System Volume Date_tested Date_analysed Swim Salinity Temperature Resting_kg Resting rsqrest Max_kg Max rsqmax AAS_kg AAS Notes True_resting
108 8 CARL369 CARL349 Arlington reef 216 0.0008961 ch1 Asus 0.04565 2023-05-20 2024-06-14 good/good 36 28.5 278.0075 0.2491225 0.9916 914.9422 0.8198797 0.9975943 636.9347 0.5707572 check max
### Expor ting data
resp_results_juveniles <- read_csv("resp_results_juveniles.csv") 
## Rows: 77 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
resp_results_juveniles <- rbind(resp_results_juveniles, results) 
resp_results_juveniles 
write.csv(resp_results_juveniles, file="./resp_results_juveniles.csv", row.names = FALSE)